Scheduling parity checks for increased throughput in early-termination, layered decoding of QC-LDPC codes on a stream processor
- JaWone A Kennedy^{1} and
- Daniel L Noneaker^{1}Email author
https://doi.org/10.1186/1687-1499-2012-141
© Kennedy and Noneaker; licensee Springer. 2012
Received: 16 May 2011
Accepted: 12 April 2012
Published: 12 April 2012
Abstract
A stream processor is a power-efficient, high-level-language programmable option for embedded applications that are computation intensive and admit high levels of data parallelism. Many signal-processing algorithms for communications are well matched to stream-processor architectures, including partially parallel implementations of layered decoding algorithms such as the turbo-decoding message-passing (TDMP) algorithm. Communication among clusters of functional units in the stream processor impose a latency cost during both the message-passing phase and the parity-check phase of the TDMP algorithm with early termination; the inter-cluster communications latency is a significant factor in limiting the throughput of the decoder. We consider two modifications of the schedule for the TDMP algorithm with early termination; each halves the communication required between functional-unit clusters of the stream processor in each iteration. We show that these can provide a substantial increase in the information throughput of the decoder without increasing the probability of error.
Keywords
1 Introduction
Quasi-cyclic (QC) low-density parity-check (LDPC) codes [1] based on circulant permutation submatrices are used for forward error correction in a wide variety of wireless communication systems employing battery-powered devices, including WiMAX (802.16) [2] and Wi-Fi (802.11 n) [3] networks. The embedded processors in the devices must satisfy stringent limits on their power consumption, yet they must also exploit the inherent parallelism in the belief-propagation decoding algorithms [1] used with LDPC codes in order to achieve a high decoder throughput. The twin performance objectives typically result in hardware designs using application specific integrated circuits or field-programmable gate arrays instead of general-purpose digital signal processors (DSPs).
An alternative is the use of a specialized DSP that is optimized for a high level of single-instruction, multiple-data (SIMD) parallelism. Such a processor can provide the high-level-language programmability of a general-purpose DSP but with a much greater computational throughput relative to power dissipation for algorithms that admit high levels of data parallelism. An example of a SIMD-centric DSP is the graphical processing unit (GPU). GPUs are powerful, multi-core processors that provide a combination of task parallelism, thread parallelism, and data parallelism which can be used for high-throughput decoding of LDPC codes [4, 5]. GPUs exhibit high power consumption as a result of floating-point processing and support of highly multithreaded task-level parallelism (including the consequent structure of the memory hierarchy [6]), however; thus a GPU is impractical for use in a battery-powered mobile communication device or an unattended sensor node.
An emerging alternative for computationally demanding digital signal processing is a SIMD-optimized DSP designed for embedded systems, exemplified by the stream processor (or on-chip stream co-processor) [7]. A stream processor is a high-data-width SIMD architecture with a software-managed, cacheless memory hierarchy that adheres to the stream-processing computing paradigm [7]; it is designed for applications that exhibit compute intensity, data parallelism, and producer-consumer locality [6]. A stream processor provides a more efficient trade-off between SIMD parallelism and power consumption than a GPU by supporting only fixed-point arithmetic for single-task, single-threaded execution, emphasizing greater local data-memory density around the functional units, and by omitting some specialized functions found in the GPU. The fixed-point processor may provide two or more data-resolution modes operating on packed-data operands so that lower-resolution arithmetic operations can be used to achieve greater computational throughput.
The stream-processor architecture is well-suited to decoding a QC-LDPC code using the turbo-decoding message-passing (TDMP) algorithm. The TDMP algorithm [8, 9] is a rowwise, layered-decoding belief-propagation algorithm [10] that is readily adapted to differing levels of partially parallel computation based on the level of parallelism available in the processor. It achieves performance comparable to the sum-product algorithm (SPA) with about one-half the average number of iterations. The TDMP algorithm requires less memory than the SPA [8], and it permits simpler data management than the SPA in processor architectures organized for high levels of SIMD parallelism [11]. Previous research has addressed LDPC decoding with the SPA [1] using floating-point arithmetic on a GPU [4, 5]. Low-power, fixed-point stream processors present different decoder design tradeoffs than GPUs, however, and the TDMP algorithm entails different design considerations than the SPA.
In this article, we investigate alternatives for early-termination decoding of QC-LDPC codes on a stream processor using a form of the TDMP algorithm which achieves good performance with low-resolution, fixed-point arithmetic. (Early termination increases the throughput of the decoder by allowing it to exit the decoding algorithm prior to the maximum number of allowed iterations if the decoded word passes all parity checks.) We consider two algorithms in which the posterior updates and parity checks are integrated for each subset of the check nodes processed in parallel, in contrast with the standard TDMP schedule in which all parity checks for an iteration are performed after all the updates. The decoding algorithms reduce by half the data communications required between the stream processor's functional-unit clusters for each iteration of the decoder using the standard schedule. Termination rules which guarantee a valid decoded word are also specified for each integrated update-and-parity-check decoding algorithm. The probability of error and the throughput achieved with each decoding algorithm is evaluated. It is shown that properly designed integration of the update and parity-check steps results in significantly higher decoder throughput than the standard schedule of the TDMP algorithm without an increase in the decoder's probability of error.
2 The constrained-update offset-min-sum TDMP algorithm
where {n_{ i }} are i.i.d., zero-mean Gaussian random variables with variance σ^{2} = N_{0}/(2E_{ s }) and E_{ s }is the received energy per channel symbol. (The corresponding energy per bit of information is denoted by E_{ b }.)
Each iteration of the TDMP algorithm is applied to an M × N parity-check matrix H of an (N, N - M) LDPC code, with updates of posterior values for variable nodes (each corresponding to a code symbol) performed in a block-sequential manner. Concurrent updates use message passing based on the parity-check equations corresponding to a blocks of rows of H[9]. Extrinsic messages that are generated from decoding earlier row blocks are used as input prior messages for updates of the posterior values for variables nodes participating in later row blocks. The row-wise update schedule thus differs from the SPA in which all check-node updates occur before the updates of the posterior values are performed for any variable nodes in an iteration.
We consider application of the offset-min-sum approximation [12] to the TDMP algorithm, which yields floating-point decoder performance comparable to the original TDMP algorithm with substantially lower decoding complexity. The use of 8-bit arithmetic can provide up to twice the computational throughput of 16-bit arithmetic in SIMD architectures allowing multiple packed-data arithmetic modes. The use of 8-bit, fixed-point saturating arithmetic can result in a dramatic increase in the probability of error in offset-min-sum TDMP decoding compared with floating-point processing or 16-bit fixed-point processing, however, due to frequent saturation of posterior values in the algorithm if the maximum number of decoding iterations is large [11]. (The offset-min-sum variant of the SPA is much less sensitive to the fixed-point resolution, as seen in [13], for example.) The use of a properly chosen constraint on the magnitude of any extrinsic update in the algorithm mitigates the effect of saturation, however, and results in performance of the TDMP algorithm with 8-bit processing comparable to the performance obtained with 16-bit processing [11]. Thus we consider the constrained-update, offset-min-sum variant of the TDMP algorithm in this investigation. In the remainder of the article, it is referred to simply as the "TDMP algorithm".
In the following description of the TDMP algorithm, the vector ${\underset{\xaf}{\mathbf{\lambda}}}^{i}=\left[{\lambda}_{1}^{i},...,{\lambda}_{{c}_{i}}^{i}\right]$ represents the extrinsic messages that correspond to the nonzero entries in row i of H, where c_{ i }represents the row weight of row i. The notation I_{ i }denotes a list of the column positions of non-zero entries in row i in H. The vector $\underset{\xaf}{\gamma}=[{\gamma}_{1},\mathrm{...},{\gamma}_{N}]$ represents the N posterior values, one for each code symbol v_{ i }. The subset of the posterior messages corresponding to the non-zero column positions of row i are denoted $\underset{\xaf}{\gamma}\left({I}_{i}\right)$.
- 1.
Initialize ${\underset{\xaf}{\mathbf{\lambda}}}^{i}=\underset{\xaf}{0}$, for i = 1, ..., M. Also, initialize the posterior values $\underset{\xaf}{\gamma}=\left[{r}_{i},...,{r}_{n}\right]$, where r _{ i }is the real-valued channel output for v _{ i }. Each entry in $\underset{\xaf}{\gamma}$ is applied to a uniform quantizer with quantization interval Δ (and clipping) for subsequent fixed-point processing using a signed, 8-bit representation.
- 2.
Read the extrinsic messages ${\underset{\xaf}{\mathbf{\lambda}}}^{i}$ and the posterior values $\underset{\xaf}{\gamma}\left({I}_{i}\right)$ for row i.
- 3.
Subtract ${\underset{\xaf}{\mathbf{\lambda}}}^{i}$ from $\underset{-}{\gamma}\left({I}_{i}\right)$ to generate prior messages $\underset{\xaf}{\mathit{\rho}}=\left[{\rho}_{1},...,{\rho}_{{c}_{i}}\right]=\underset{\xaf}{\gamma}\left({I}_{i}\right)-{\underset{\xaf}{\mathit{\lambda}}}^{i}$.
- 4.Decode the parity-check equation for row i. Define $\underset{\xaf}{\mathit{\alpha}}=\left[{\alpha}_{1},...,{\alpha}_{{c}_{i}}\right]$ and $\underset{\xaf}{\mathit{\beta}}=\left[{\beta}_{1},...,{\beta}_{{c}_{i}}\right]$, where α _{ j }= sgn[ρ _{ j }] (the sign of ρ _{ j }) and β _{ j }= |ρ _{ j }|. Set${\lambda}_{j}^{i}=\left(\prod _{k=1,k\ne j}^{{c}_{i}}{\alpha}_{k}\right)\cdot \text{max}\left(\underset{1\le k\le {c}_{i},k\ne j}{\text{min}}{\beta}_{k}-\eta ,0\right)$
for j = 1, ..., c_{ i }, where the offset η is a non-negative constant that is an integer multiple of the quantization interval. (The values of Δ and η are chosen jointly to minimize the error probability at some operating point of interest.)
- 5.Limit the maximum extrinsic updates in Step 4 to${\lambda}_{j}^{i}=\mathsf{\text{sgn}}\phantom{\rule{1em}{0ex}}\left[{\lambda}_{j}^{i}\right]\cdot \text{min}\left(\left|{\lambda}_{j}^{i}\right|,\epsilon \right)$
where ϵ is a predetermined constant that is used to limit saturation in the posteriors. It is also an integer multiple of the quantization interval.
- 6.
Update the posterior values for the code-symbol positions of I _{ i }as $\underset{\xaf}{\gamma}\left({I}_{i}\right)=\underset{\xaf}{\rho}+{\underset{\xaf}{\mathit{\lambda}}}^{i}$.
- 7.
Steps 2-6 represent a decoding subiteration for one row of H. Repeat the steps for each row of H.
For architecture-aware LDPC codes [8] (such as QC-LDPC codes based on circulant permutation submatrices), large sets of consecutive rows of H correspond to checks nodes with disjoint sets of variable nodes. Steps 2-6 can be executed in parallel for any such set of rows without altering the logic of the algorithm.
Steps 2-7 represent the message-passing phase of one iteration of the TDMP algorithm. It is followed by the parity-check phase in which a hard decision is made on each code symbol v_{ i }based upon the sign of γ_{ i }and each independent parity check is tested. If all the parity checks are satisfied, decoding is terminated with a valid decoded code word, and the information bits are recovered by inverse mapping. If not, but the maximum number of iterations has been executed, a known decoding failure occurs. Otherwise, another iteration of the algorithm is performed. We use this schedule of updates and parity checks for the TDMP algorithm (with alternating message-passing and parity-check phases) as a benchmark in the article and refer to it as the TDMP algorithm with the standard schedule.
3 An example of a stream processor
The Storm-1 system includes two general-purpose processors and a separate data-processing unit (DPU) with a SIMD architecture. The DPU contains 80 ALUs organized as 16 data-parallel 5-ALU functional-unit clusters referred to as lanes that are controlled by a very long instruction word in a Harvard architecture. The Storm-1 processor contains a three-level, non-caching memory hierarchy. A large off-chip memory is accessible to the DPU. Lane register files are high-bandwidth, per-lane, on-chip memory used to stage data for processing by the ALUs. Operand register files within each lane's ALU cluster, addressable only within the cluster, serve as local registers. Data can also be exchanged directly between ALU clusters using an inter-lane cross-bar switch. The accompanying compiler allows an application programmer to exploit available data parallelism and instruction parallelism without the need to explicitly manage the resources of the lanes and the associated memory.
The DPU is designed for the flow of similarly formatted records of a large data set which form a stream. The stream is processed by one or more kernel functions, each of which is a compute-intensive inner loop that applies parallel processing to a stream that is resident in on-chip memory. The computations in the parallel-processing units are thus restricted to records in the stream as atomic data units and kernel functions as atomic instructions. Control tasks and computations that do not fit well within this stream-processing paradigm are assigned by the compiler to a general-purpose coprocessor.
Analysis by the compiler establishes the stream allocation and run-time data transfers into on-chip memory for kernels and streams based on dependencies associated with execution. Parallel processing occurs within kernels only; consequently, single structured instruction flow is preserved. Kernels are managed by the compiler, and they can form pipelines which share intermediate stream results. Data reference by a kernel is limited to the data records it is processing and other locally retained constants and variables. The compile-time analysis imposes a tight control on the use of the memory hierarchy, hiding the access latency to external memory for many tasks.
The simplicity of the stream-processing application programming model is achieved at the cost of restrictions in the computation model and strict management of the memory hierarchy. The resources of the parallel-processing architecture are utilized most efficiently if the application has characteristics consistent with these constraints. Specifically, the application should exhibit compute intensity, data-parallelism, and data locality [6]. Many signal-processing algorithms exhibit these characteristics.
The Storm-1 processor provides 8-bit packed-data and 16-bit packed-data modes of saturating fixed-point arithmetic using 32-bit data registers. The production device was fabricated in a 130 nm process. It employs a nominal DPU clock rate of 800 MHz, resulting in an aggregate computation rate of 512 8-bit fixed-point GOPS (billion operations per second) or 256 16-bit fixed-point GOPS [14]. An evaluation board which operates at a lower clock rate is used (along with an emulator) to obtain the decoder performance measurements discussed in subsequent sections, but the results are given for the clock rate of the production device. The programming language used for application development for the Storm-1 processor is based on ANSI "C" with enhancements that define the kernel functions and streams as well as several compiler directives (pragmas).
4 Implementing the offset-min-sum TDMP algorithm on a stream processor
The TDMP algorithm is well suited to implementation on the stream processor if it is used with LDPC codes in which large groups of consecutive row updates can be performed in parallel without data conflicts. For example, a QC-LDPC code with circulant permutation submatrices permits a degree of parallelism at least as great as the row dimension of the submatrices. Each block of parallel row updates forms one subiteration of a decoder iteration. In this circumstance, the algorithm is characterized by a high level of available data parallelism and a high level of data locality. The compute intensity is only moderate, but data exchanges are limited to the high-speed inter-lane crossbar switch.
A single compiler directive for the Storm-1 processor can specify up to 64 concurrent data-parallel computations (four per lane) by using its 8-bit packed-data mode. This allows simultaneous processing of four check nodes per lane which yields a total of 64 concurrent row updates across the 16 lanes of the processor. Thus the row-update data parallelism available with the compiler and the architecture in 8-bit mode is fully exploited if the dimension of the permutation submatrices is an integer multiple of 64.
The five ALU's per lane also provide the compiler with the opportunity for instruction-level parallelism in the calculations associated with each row update, including parallel updates of as many as five posterior values for each row in Steps 2-6 of the algorithm. Since most of the QC-LDPC codes of interest have a parity-check matrix with a weight of five or more for most rows, an assignment of four concurrent row updates to each lane should result in a high utilization of the processor's 320 ALUs in 8-bit mode. (The diagnostic tools provided with the Storm-1 evaluation board do not allow measurement of the VLIW packing ratio or the ALU utilization to assess the average level of instruction-level parallelism achieved by the decoding algorithm, however.)
As a result of the match between the available parallelism in the LDPC code's parity-check matrix and the SIMD parallelism of the processor, straightforward programming of the TDMP decoding algorithm results in an implementation in which extrinsic messages and updates for a given row of the parity-check matrix are managed by the same lane of the processor throughout the decoding of a code word. Consequently, information defining the variable-node participation in a given row is loaded once into the operand register files of the responsible lane and retained for the duration of decoding. The structure of the parity-check matrix permits its representation in a form amenable to efficient loading and storing, which we exploit (and which is exploited elsewhere in implementations on GPUs [5, 18, 19]).
The posterior value for each variable node must be communicated to another lane after it is updated in a subiteration, in general, where the recipient lane is the one that requires the value in the most immediate future subiteration. Most of the latency for these data transfers can be hidden during the message-passing phase (Steps 2-7) of the TDMP algorithm due to the computation required during the phase. The same posterior transfers must occur during the parity-check phase, however, and the lower computational load in that phase exposes most of the latency as a decoding delay. As shown in the next section, the exposed inter-lane communication latency can be a significant factor in limiting the throughput of the TDMP decoder using the standard schedule consisting of separate message-passing and parity-check phases in each iteration.
Imperfect regularity in the data structures employed by the TDMP decoder can result in conditional execution and branching which markedly reduces the average utilization of the processor's resources. These problems arise if the LDPC code is irregular in the row weights of the parity-check matrix, such as the WiMAX codes we consider in our examples. (The same observation is noted in [5] regarding decoding irregular LDPC codes on a GPU.) We address this problem by adding "dummy" variable-node positions to the representation of the parity-check matrix and corresponding dummy circulant permutation submatrices to each row-block containing fewer than the maximum number of non-zero submatrices. The apparent row-weights of the code are consequently "regularized", which eliminates the need for conditional execution of instructions in key parts of the code. Each dummy variable node is initialized with a prior value set to the largest possible magnitude of a binary zero in its signed, 8-bit representation. No two row-blocks contain non-zero dummy circulant submatrices in the same column positions, so each dummy variable node participates in message passing for only one row. The technique reduces the computation time per iteration at the cost of a modest increase in the size of the representation of the parity-check matrix. Simulations show it has no measurable detrimental effect on the error probability or the convergence behavior of the decoding algorithm.
The TDMP decoder is implemented as a single kernel function on the Storm-1 processor. Each input stream corresponds to the vector of quantized channel samples for one code word, and each output stream corresponds to one detected code word. The instruction kernel and the information defining the code's parity-check matrix are loaded from the global memory into the DPU only once, at the start of decoding; both are retained in the DPU throughout the processing of many consecutive streams (i.e., while decoding many code words). In our implementation, the transfer of the channel samples to the DPU's lane register files from the general-purpose processor occurs while the DPU is otherwise idle. The resulting latency is a negligible fraction of the decoding time for a code word, however.
5 Alternatives for early termination of the offset-min-sum TDMP algorithm on a stream processor
Early termination can be achieved with the TDMP algorithm using the standard schedule, in which each iteration consists of a phase with message passing for all rows of the parity-check matrix followed by a phase with parity checks for all rows of the matrix. The algorithm guarantees that either the decoder produces a valid code word at termination or declares a decoder failure after the maximum allowed number of iterations. In this section, we consider three alternatives to the standard schedule for the offset-min-sum TDMP algorithm with early termination. Two of them are alternatives of practical interest that provide the same correctness guarantee as the standard schedule. The other alternative uses a naive approach that does not guarantee a valid decoded code word. The decoder with the standard schedule and the decoder using the naive approach provide two benchmarks against which we evaluate the performance of the two practical alternatives to the standard schedule.
The performance of the algorithms is illustrated for four WiMAX-standard codes [2], each of which is a QC-LDPC code composed of circulant permutation submatrices of size 64. The block length of each code is 1536, and the rates of the codes are 1/2, 2/3, 3/4, and 5/6. (The rate-2/3 code and the rate-3/4 code are constructed using base-matrix option A in [2].)
The performance of TDMP decoding is evaluated for each code with the Storm-1 processor using its 8-bit mode for saturating, fixed-point arithmetic. The parameters of the decoding algorithm for each code are chosen to provide nearly-optimal performance with the standard schedule. In each instance, a quantization interval of Δ = 0.125, an offset of η = 0.125, and an extrinsic-update magnitude constraint of ϵ = 2.5 yield the lowest probability of error across signal-to-noise ratios of interest. A maximum of twenty decoder iterations is allowed for any code word, which is sufficient to provide most of the performance achievable with TDMP decoding for the four codes. Decoder execution times are evaluated with the processor decoding a sequence of consecutive code words as would be required in a practical communication system.
The decoding time per code word using the standard schedule is determined by the time required for the three components of decoding. The first component is the set-up and completion that includes the time required to initialize the DPU before the first iteration and the time required to recover decoded data after the last iteration. The other two components represent the time for the message-passing phase of the TDMP algorithm per iteration and the time for the parity-check phase per iteration. The decoding time for a code word using the standard schedule is thus given by the set-up-and-completion time plus the number of decoding iterations multiplied by the sum of the message-passing time and the parity-check time for an iteration.
Processing time of decoding components for the rate-1/2 WiMAX code
Decoding algorithm | Set-up & completion | Message passing Only (per iteration) | Parity checks only (per iteration) | Integrated message passing & parity checks (per iteration) |
---|---|---|---|---|
Standard schedule | 2.13 μ s | 2.05 μ s | 1.28 μ s | - |
Naive IPC | 2.13 μ s | - | - | 2.13 μ s |
IPC/confirmation | 2.13 μ s | - | 1.28 μ s | 2.13 μ s |
IPC/stability check | 2.13 μ s | - | - | 2.15 μ s |
The processing time per iteration can be reduced substantially if some means is employed to eliminate the inter-lane communications associated with performing parity checks. Consider an alternative approach to testing the correctness of code-symbol polarities in which the parity checks for a given block of rows are incorporated within the corresponding message-passing subiterations. At the end of each subiteration of the message-passing phase, the posteriors updated during the subiteration are used to determine if the corresponding subset of parity checks are satisfied. The current posterior values required for each parity check are already located in the stream-processor lane in which the parity check is performed since the updates of the same values have just been completed in the same lane. Thus the need for separate inter-lane communications for parity checks is eliminated. We refer to this non-standard schedule of parity checks as the integrated parity check (IPC). The decoding time for the IPC is shown in the second-row entry in Table 1. The processing time for the integrated message-passing-and-parity-check phase of an iteration is only .08 μ s greater than the message-passing phase alone in the standard schedule.
First consider a decoder in which the IPC is used and decoding is terminated after an iteration in which the integrated parity checks are satisfied during all message-update subit-erations. (There is no separate parity-check phase and thus no processing time allocated to such a phase.) The elimination of the parity-check phase in each iteration results in a substantial reduction in the processing time per iteration compared with decoding using the standard schedule. The algorithm does not guarantee that a valid code word is decoded at termination, however; thus, we refer to it as the naive IPC.
The validity of the decoded code word is guaranteed if the naive IPC is supplemented by a standard parity-check phase which is employed beginning with the first iteration in which all the integrated parity checks are satisfied. This modified IPC is referred to as the IPC with confirmation. Alternatively, the same validity guarantee is achieved if the naive IPC is modified by adding stability checks in each subiteration. At the end of the subiteration, the lane performing the updates for a given row of the parity-check matrix determines if the updates have changed the sign of the posterior value for any variable node participating in the corresponding parity check. (I.e., it determines if the update has changed the tentative hard decision for any corresponding code symbol.)
The stability checks add .02 μ s to the processing time for the message-passing phase of an iteration compared with the naive IPC and the IPC with confirmation, as shown in Table 1. If both the integrated parity checks and the stability checks are satisfied for each message-update subiteration in an iteration, the hard decisions at the end of the iteration are guaranteed to correspond to a valid code word (without the requirement of a separate parity-check phase). This modified IPC is referred to as the IPC with stability check. The stability-check technique has been pointed out previously in [10, 20], though not in the context of implementation on a stream processor. (It is referred to as an "on-the-fly convergence check" in the latter article.) No evaluation of its impact on the decoding time is provided in either article.
As seen in Figure 2, both the IPC with confirmation and the IPC with stability check result in the same probability of code-word error as the standard schedule for the rate-1/2 WiMAX code. (For all three algorithms, almost all code-word errors are known decoder failures.) The probability of code-word error with each schedule is 10^{-3} if the signal-to-noise ratio is 1.97 dB, and it is 10^{-4} if the signal-to-noise ratio is 2.18 dB. Agreement of the error probabilities for the three schedules is also observed when considering the probability of bit error and when comparing the performance for the other three WiMAX codes.
Information throughput without early termination for the rate-1/2 WiMAX code
Decoding algorithm | I = 2 | I = 6 | I = 10 | I = 20 |
---|---|---|---|---|
Naive IPC (Mbps) | 120.4 | 51.6 | 32.9 | 17.2 |
IPC w/stability check (Mbps) | 119.6 | 51.2 | 32.6 | 17.1 |
IPC w/confirmation (Mbps) | 100.2 | 47.5 | 31.1 | 16.7 |
Standard schedule (Mbps) | 87.4 | 34.7 | 21.7 | 11.2 |
An average of 5.4 iterations are required by the IPC with confirmation for the channel which results in a probability of code-word error of 10^{-}^{4}. This schedule requires approximately 0.2-0.3 iterations more than is required by the standard schedule on average for any channel quality of practical interest. It follows from the definition of the algorithms that the naive IPC must result in a lower average number of iterations and higher throughput than either the IPC with confirmation or the IPC with stability check, but any other comparison of the throughputs of the schedules requires evaluation by execution on the processor or simulation in conjunction with Table 1. The average number of iterations required with each schedule approaches one asymptotically with improving channel quality, but the IPC with stability check approaches its limiting behavior more slowly than either the IPC with confirmation or the standard schedule.
The IPC with confirmation provides a slightly greater throughput than the IPC with stability check with the rate-1/2 WiMAX code for signal-to-noise ratios between 1 dB and 5 dB. In the limit as the signal-to-noise ratio approaches infinity (in which each decoding attempt requires only one iteration), however, the throughput for both the standard schedule and the IPC with confirmation is approximately 140 Mbps, whereas the limiting throughput for both the naive IPC and the IPC with stability check is nearly 180 Mbps. (Note that accurate simulated or emulated results for the average number of iterations can be obtained using a feasible number of sample outcomes at a higher signal-to-noise ratio than is feasible for accurately measuring the probability of code-word error.)
Information throughput for the WiMAX code of each rate
P _{block} | 10^{-4} | 10^{-3} | ||||||
---|---|---|---|---|---|---|---|---|
Rate | 1/2 | 2/3 | 3/4 | 5/6 | 1/2 | 2/3 | 3/4 | 5/6 |
IPC w/ | ||||||||
stability check (Mbps) IPC w/ | 50.4 | 88.9 | 100.0 | 146.0 | 46.0 | 79.7 | 91.7 | 131.6 |
confirmation (Mbps) Standard | 51.3 | 88.9 | 101.0 | 140.9 | 46.8 | 79.7 | 93.0 | 128.9 |
schedule (Mbps) | 40.3 | 74.4 | 83.3 | 129.3 | 36.0 | 64.4 | 79.0 | 111.4 |
Another characteristic of the decoder implementation which is of interest is the memory occupied by its executable code. The DPU in the Storm-1 processor includes an instruction memory of 2,048 384-bit instruction words. In our implementation, the rate-1/2 decoder using the IPC with stability check occupies only 21.2% of the instruction memory, whereas the rate-1/2 decoders using the standard schedule and the IPC with confirmation occupy 24.2% and 25.1% of the instruction memory, respectively. The decoder using the IPC with stability check requires 23.0%, 28.4%, and 76.4% of the instruction memory for the codes of rates 2/3, 3/4, and 5/6, respectively. Over all four codes, 7%-21% more instruction memory is required if the standard schedule is used than if the IPC with stability check is used. Similarly, 10%-28% more instruction memory is required if the IPC with confirmation is used than if the IPC with stability check is used. The greater instruction-memory efficiency of the decoder using the IPC with stability check is the result of the absence of a separate parity-check phase in the schedule.
6 Conclusions
The data-level parallelism of a low-power, embedded stream processor can be used to accelerate the decoding of a QC-LDPC code using variants of the TDMP layered belief-propagation algorithm. The communications among functional-unit clusters of the processor is a significant factor in the decoding time using the standard approach of alternating message-passing and parity-check phases for TDMP decoding. An alternative approach in which the parity checks are integrated with the message-passing phase reduces the exposed communication latency within the processor. Either a separate parity-check confirmation phase or a check for stability of hard decisions throughout an iteration of the message-passing phase can be used to ensure a valid code word at termination of the modified algorithm. Both provide a substantial increase in the decoder throughput over the standard TDMP schedule at no cost in the error probability for decoding with early termination. The algorithm employing stability checks permits the most efficient use of instruction memory due to the absence of a separate parity-check phase.
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References
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