System-on-chip Computing and Interconnection Architectures for Telecommunications and Signal Processing

This dissertation proposes novel architectures and design techniques targeting SoC building blocks for telecommunications and signal processing applications. Hardware implementation of Low-Density Parity-Check decoders is approached at both the algorithmic and the architecture level. Low-Density Parity-Check codes are a promising coding scheme for future communication standards due to their outstanding error correction performance. This work proposes a methodology for analyzing effects of finite precision arithmetic on error correction performance and hardware complexity. The methodology is throughout employed for co-designing the decoder. First, a low-complexity check node based on the P-output decoding principle is designed and characterized on a CMOS standard-cells library. Results demonstrate implementation loss below 0.2 dB down to BER of 10^{-8} and a saving in complexity up to 59% with respect to other works in recent literature. High-throughput and low-latency issues are addressed with modified single-phase decoding schedules. A new "memory-aware" schedule is proposed requiring down to 20% of memory with respect to the traditional two-phase flooding decoding. Additionally, throughput is doubled and logic complexity reduced of 12%. These advantages are traded-off with error correction performance, thus making the solution attractive only for long codes, as those adopted in the DVB-S2 standard. The "layered decoding" principle is extended to those codes not specifically conceived for this technique. Proposed architectures exhibit complexity savings in the order of 40% for both area and power consumption figures, while implementation loss is smaller than 0.05 dB. Most modern communication standards employ Orthogonal Frequency Division Multiplexing as part of their physical layer. The core of OFDM is the Fast Fourier Transform and its inverse in charge of symbols (de)modulation. Requirements on throughput and energy efficiency call for FFT hardware implementation, while ubiquity of FFT suggests the design of parametric, re-configurable and re-usable IP hardware macrocells. In this context, this thesis describes an FFT/IFFT core compiler particularly suited for implementation of OFDM communication systems. The tool employs an accuracy-driven configuration engine which automatically profiles the internal arithmetic and generates a core with minimum operands bit-width and thus minimum circuit complexity. The engine performs a closed-loop optimization over three different internal arithmetic models (fixed-point, block floating-point and convergent block floating-point) using the numerical accuracy budget given by the user as a reference point. The flexibility and re-usability of the proposed macrocell are illustrated through several case studies which encompass all current state-of-the-art OFDM communications standards (WLAN, WMAN, xDSL, DVB-T/H, DAB and UWB). Implementations results are presented for two deep sub-micron standard-cells libraries (65 and 90 nm) and commercially available FPGA devices. Compared with other FFT core compilers, the proposed environment produces macrocells with lower circuit complexity and same system level performance (throughput, transform size and numerical accuracy). The final part of this dissertation focuses on the Network-on-Chip design paradigm whose goal is building scalable communication infrastructures connecting hundreds of core. A low-complexity link architecture for mesochronous on-chip communication is discussed. The link enables skew constraint looseness in the clock tree synthesis, frequency speed-up, power consumption reduction and faster back-end turnarounds. The proposed architecture reaches a maximum clock frequency of 1 GHz on 65 nm low-leakage CMOS standard-cells library. In a complex test case with a full-blown NoC infrastructure, the link overhead is only 3% of chip area and 0.5% of leakage power consumption. Finally, a new methodology, named metacoding, is proposed. Metacoding generates correct-by-construction technology independent RTL codebases for NoC building blocks. The RTL coding phase is abstracted and modeled with an Object Oriented framework, integrated within a commercial tool for IP packaging (Synopsys CoreTools suite). Compared with traditional coding styles based on pre-processor directives, metacoding produces 65% smaller codebases and reduces the configurations to verify up to three orders of magnitude

Design of hardware accelerators for demanding applications.

This paper focuses on mastering the architecture development of hardware accelerators. It presents the results of our analysis of the main issues that have to be addressed when designing accelerators for modern demanding applications, when using as an example the accelerator design for LDPC decoding for the newest demanding communication system standards. Based on the results of our analysis, we formulate the main requirements that have to be satisfied by an adequate accelerator design methodology, and propose a design approach which satisfies these requirements

Design of hardware accelerators for demanding applications.

This paper focuses on mastering the architecture development of hardware accelerators. It presents the results of our analysis of the main issues that have to be addressed when designing accelerators for modern demanding applications, when using as an example the accelerator design for LDPC decoding for the newest demanding communication system standards. Based on the results of our analysis, we formulate the main requirements that have to be satisfied by an adequate accelerator design methodology, and propose a design approach which satisfies these requirements

Concatenated Turbo/LDPC codes for deep space communications: performance and implementation

Deep space communications require error correction codes able to reach extremely low bit-error-rates, possibly with a steep waterfall region and without error floor. Several schemes have been proposed in the literature to achieve these goals. Most of them rely on the concatenation of different codes that leads to high hardware implementation complexity and poor resource sharing. This work proposes a scheme based on the concatenation of non-custom LDPC and turbo codes that achieves excellent error correction performance. Moreover, since both LDPC and turbo codes can be decoded with the BCJR algorithm, our preliminary results show that an efficient hardware architecture with high resource reuse can be designe

Algorithm Development and VLSI Implementation of Energy Efficient Decoders of Polar Codes

With its low error-floor performance, polar codes attract significant attention as the potential standard error correction code (ECC) for future communication and data storage. However, the VLSI implementation complexity of polar codes decoders is largely influenced by its nature of in-series decoding. This dissertation is dedicated to presenting optimal decoder architectures for polar codes. This dissertation addresses several structural properties of polar codes and key properties of decoding algorithms that are not dealt with in the prior researches. The underlying concept of the proposed architectures is a paradigm that simplifies and schedules the computations such that hardware is simplified, latency is minimized and bandwidth is maximized. In pursuit of the above, throughput centric successive cancellation (TCSC) and overlapping path list successive cancellation (OPLSC) VLSI architectures and express journey BP (XJBP) decoders for the polar codes are presented. An arbitrary polar code can be decomposed by a set of shorter polar codes with special characteristics, those shorter polar codes are referred to as constituent polar codes. By exploiting the homogeneousness between decoding processes of different constituent polar codes, TCSC reduces the decoding latency of the SC decoder by 60% for codes with length n = 1024. The error correction performance of SC decoding is inferior to that of list successive cancellation decoding. The LSC decoding algorithm delivers the most reliable decoding results; however, it consumes most hardware resources and decoding cycles. Instead of using multiple instances of decoding cores in the LSC decoders, a single SC decoder is used in the OPLSC architecture. The computations of each path in the LSC are arranged to occupy the decoder hardware stages serially in a streamlined fashion. This yields a significant reduction of hardware complexity. The OPLSC decoder has achieved about 1.4 times hardware efficiency improvement compared with traditional LSC decoders. The hardware efficient VLSI architectures for TCSC and OPLSC polar codes decoders are also introduced. Decoders based on SC or LSC algorithms suffer from high latency and limited throughput due to their serial decoding natures. An alternative approach to decode the polar codes is belief propagation (BP) based algorithm. In BP algorithm, a graph is set up to guide the beliefs propagated and refined, which is usually referred to as factor graph. BP decoding algorithm allows decoding in parallel to achieve much higher throughput. XJBP decoder facilitates belief propagation by utilizing the specific constituent codes that exist in the conventional factor graph, which results in an express journey (XJ) decoder. Compared with the conventional BP decoding algorithm for polar codes, the proposed decoder reduces the computational complexity by about 40.6%. This enables an energy-efficient hardware implementation. To further explore the hardware consumption of the proposed XJBP decoder, the computations scheduling is modeled and analyzed in this dissertation. With discussions on different hardware scenarios, the optimal scheduling plans are developed. A novel memory-distributed micro-architecture of the XJBP decoder is proposed and analyzed to solve the potential memory access problems of the proposed scheduling strategy. The register-transfer level (RTL) models of the XJBP decoder are set up for comparisons with other state-of-the-art BP decoders. The results show that the power efficiency of BP decoders is improved by about 3 times

Algorithm Development and VLSI Implementation of Energy Efficient Decoders of Polar Codes

With its low error-floor performance, polar codes attract significant attention as the potential standard error correction code (ECC) for future communication and data storage. However, the VLSI implementation complexity of polar codes decoders is largely influenced by its nature of in-series decoding. This dissertation is dedicated to presenting optimal decoder architectures for polar codes. This dissertation addresses several structural properties of polar codes and key properties of decoding algorithms that are not dealt with in the prior researches. The underlying concept of the proposed architectures is a paradigm that simplifies and schedules the computations such that hardware is simplified, latency is minimized and bandwidth is maximized. In pursuit of the above, throughput centric successive cancellation (TCSC) and overlapping path list successive cancellation (OPLSC) VLSI architectures and express journey BP (XJBP) decoders for the polar codes are presented. An arbitrary polar code can be decomposed by a set of shorter polar codes with special characteristics, those shorter polar codes are referred to as constituent polar codes. By exploiting the homogeneousness between decoding processes of different constituent polar codes, TCSC reduces the decoding latency of the SC decoder by 60% for codes with length n = 1024. The error correction performance of SC decoding is inferior to that of list successive cancellation decoding. The LSC decoding algorithm delivers the most reliable decoding results; however, it consumes most hardware resources and decoding cycles. Instead of using multiple instances of decoding cores in the LSC decoders, a single SC decoder is used in the OPLSC architecture. The computations of each path in the LSC are arranged to occupy the decoder hardware stages serially in a streamlined fashion. This yields a significant reduction of hardware complexity. The OPLSC decoder has achieved about 1.4 times hardware efficiency improvement compared with traditional LSC decoders. The hardware efficient VLSI architectures for TCSC and OPLSC polar codes decoders are also introduced. Decoders based on SC or LSC algorithms suffer from high latency and limited throughput due to their serial decoding natures. An alternative approach to decode the polar codes is belief propagation (BP) based algorithm. In BP algorithm, a graph is set up to guide the beliefs propagated and refined, which is usually referred to as factor graph. BP decoding algorithm allows decoding in parallel to achieve much higher throughput. XJBP decoder facilitates belief propagation by utilizing the specific constituent codes that exist in the conventional factor graph, which results in an express journey (XJ) decoder. Compared with the conventional BP decoding algorithm for polar codes, the proposed decoder reduces the computational complexity by about 40.6%. This enables an energy-efficient hardware implementation. To further explore the hardware consumption of the proposed XJBP decoder, the computations scheduling is modeled and analyzed in this dissertation. With discussions on different hardware scenarios, the optimal scheduling plans are developed. A novel memory-distributed micro-architecture of the XJBP decoder is proposed and analyzed to solve the potential memory access problems of the proposed scheduling strategy. The register-transfer level (RTL) models of the XJBP decoder are set up for comparisons with other state-of-the-art BP decoders. The results show that the power efficiency of BP decoders is improved by about 3 times

Improve the Usability of Polar Codes: Code Construction, Performance Enhancement and Configurable Hardware

Error-correcting codes (ECC) have been widely used for forward error correction (FEC) in modern communication systems to dramatically reduce the signal-to-noise ratio (SNR) needed to achieve a given bit error rate (BER). Newly invented polar codes have attracted much interest because of their capacity-achieving potential, efficient encoder and decoder implementation, and flexible architecture design space.This dissertation is aimed at improving the usability of polar codes by providing a practical code design method, new approaches to improve the performance of polar code, and a configurable hardware design that adapts to various specifications. State-of-the-art polar codes are used to achieve extremely low error rates. In this work, high-performance FPGA is used in prototyping polar decoders to catch rare-case errors for error-correcting performance verification and error analysis. To discover the polarization characteristics and error patterns of polar codes, an FPGA emulation platform for belief-propagation (BP) decoding is built by a semi-automated construction flow. The FPGA-based emulation achieves significant speedup in large-scale experiments involving trillions of data frames. The platform is a key enabler of this work. The frozen set selection of polar codes, known as bit selection, is critical to the error-correcting performance of polar codes. A simulation-based in-order bit selection method is developed to evaluate the error rate of each bit using Monte Carlo simulations. The frozen set is selected based on the bit reliability ranking. The resulting code construction exhibits up to 1 dB coding gain with respect to the conventional bit selection. To further improve the coding gain of BP decoder for low-error-rate applications, the decoding error mechanisms are studied and analyzed, and the errors are classified based on their distinct signatures. Error detection is enabled by low-cost CRC concatenation, and post-processing algorithms targeting at each type of the error is designed to mitigate the vast majority of the decoding errors. The post-processor incurs only a small implementation overhead, but it provides more than an order of magnitude improvement of the error-correcting performance. The regularity of the BP decoder structure offers many hardware architecture choices. Silicon area, power consumption, throughput and latency can be traded to reach the optimal design points for practical use cases. A comprehensive design space exploration reveals several practical architectures at different design points. The scalability of each architecture is also evaluated based on the implementation candidates. For dynamic communication channels, such as wireless channels in the upcoming 5G applications, multiple codes of different lengths and code rates are needed to t varying channel conditions. To minimize implementation cost, a universal decoder architecture is proposed to support multiple codes through hardware reuse. A 40nm length- and rate-configurable polar decoder ASIC is demonstrated to fit various communication environments and service requirements.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/140817/1/shuangsh_1.pd

A High-Performance and Low-Complexity 5G LDPC Decoder: Algorithm and Implementation

5G New Radio (NR) has stringent demands on both performance and complexity for the design of low-density parity-check (LDPC) decoding algorithms and corresponding VLSI implementations. Furthermore, decoders must fully support the wide range of all 5G NR blocklengths and code rates, which is a significant challenge. In this paper, we present a high-performance and low-complexity LDPC decoder, tailor-made to fulfill the 5G requirements. First, to close the gap between belief propagation (BP) decoding and its approximations in hardware, we propose an extension of adjusted min-sum decoding, called generalized adjusted min-sum (GA-MS) decoding. This decoding algorithm flexibly truncates the incoming messages at the check node level and carefully approximates the non-linear functions of BP decoding to balance the error-rate and hardware complexity. Numerical results demonstrate that the proposed fixed-point GAMS has only a minor gap of 0.1 dB compared to floating-point BP under various scenarios of 5G standard specifications. Secondly, we present a fully reconfigurable 5G NR LDPC decoder implementation based on GA-MS decoding. Given that memory occupies a substantial portion of the decoder area, we adopt multiple data compression and approximation techniques to reduce 42.2% of the memory overhead. The corresponding 28nm FD-SOI ASIC decoder has a core area of 1.823 mm2 and operates at 895 MHz. It is compatible with all 5G NR LDPC codes and achieves a peak throughput of 24.42 Gbps and a maximum area efficiency of 13.40 Gbps/mm2 at 4 decoding iterations.Comment: 14 pages, 14 figure

Exploring High Level Synthesis to Improve the Design of Turbo Code Error Correction in a Software Defined Radio Context

With the ever improving progress of technology, Software Defined Radio (SDR) has become a more widely available technique for implementing radio communication. SDRs are sought after for their advantages over traditional radio communication mostly in flexibility, and hardware simplification. The greatest challenges SDRs face are often with their real time performance requirements. Forward error correction is an example of an SDR block that can exemplify these challenges as the error correction can be very computationally intensive. Due to these constraints, SDR implementations are commonly found in or alongside Field Programmable Gate Arrays (FPGAs) to enable performance that general purpose processors alone cannot achieve. The main challenge with FPGAs however, is in Register Transfer Level (RTL) development. High Level Synthesis (HLS) tools are a method of creating hardware descriptions from high level code, in an effort to ease this development process. In this work a turbo code decoder, a form of computationally intensive error correction codes, was accelerated with the help of FPGAs, using HLS tools. This accelerator was implemented on a Xilinx Zynq platform, which integrates a hard core ARM processor alongside programmable logic on a single chip. Important aspects of the design process using HLS were identified and explained. The design process emphasizes the idea that for the best results the high level code should be created with a hardware mindset, and written in an attempt to describe a hardware design. The power of the HLS tools was demonstrated in its flexibility by providing a method of tailoring the hardware parameters through simply changing values in a macro file, and by exploration the design space through different data types and three different designs, each one improving from what was learned in the previous implementation. Ultimately, the best hardware implementation was over 56 times faster than the optimized software implementation. Comparing the HLS to a manually optimized design shows that the HLS implementation was able to achieve over a 19% throughput, with many areas for further improvement identified, demonstrating the competitiveness of the HLS tools