Modeling and Energy Optimization of LDPC Decoder Circuits with Timing Violations

This paper proposes a "quasi-synchronous" design approach for signal processing circuits, in which timing violations are permitted, but without the need for a hardware compensation mechanism. The case of a low-density parity-check (LDPC) decoder is studied, and a method for accurately modeling the effect of timing violations at a high level of abstraction is presented. The error-correction performance of code ensembles is then evaluated using density evolution while taking into account the effect of timing faults. Following this, several quasi-synchronous LDPC decoder circuits based on the offset min-sum algorithm are optimized, providing a 23%-40% reduction in energy consumption or energy-delay product, while achieving the same performance and occupying the same area as conventional synchronous circuits.Comment: To appear in IEEE Transactions on Communication

Cross-Layer Optimization for Power-Efficient and Robust Digital Circuits and Systems

With the increasing digital services demand, performance and power-efficiency become vital requirements for digital circuits and systems. However, the enabling CMOS technology scaling has been facing significant challenges of device uncertainties, such as process, voltage, and temperature variations. To ensure system reliability, worst-case corner assumptions are usually made in each design level. However, the over-pessimistic worst-case margin leads to unnecessary power waste and performance loss as high as 2.2x. Since optimizations are traditionally confined to each specific level, those safe margins can hardly be properly exploited. To tackle the challenge, it is therefore advised in this Ph.D. thesis to perform a cross-layer optimization for digital signal processing circuits and systems, to achieve a global balance of power consumption and output quality. To conclude, the traditional over-pessimistic worst-case approach leads to huge power waste. In contrast, the adaptive voltage scaling approach saves power (25% for the CORDIC application) by providing a just-needed supply voltage. The power saving is maximized (46% for CORDIC) when a more aggressive voltage over-scaling scheme is applied. These sparsely occurred circuit errors produced by aggressive voltage over-scaling are mitigated by higher level error resilient designs. For functions like FFT and CORDIC, smart error mitigation schemes were proposed to enhance reliability (soft-errors and timing-errors, respectively). Applications like Massive MIMO systems are robust against lower level errors, thanks to the intrinsically redundant antennas. This property makes it applicable to embrace digital hardware that trades quality for power savings.Comment: 190 page

Scalable Energy-Recovery Architectures.

Energy efficiency is a critical challenge for today's integrated circuits, especially for high-end digital signal processing and communications that require both high throughput and low energy dissipation for extended battery life. Charge-recovery logic recovers and reuses charge using inductive elements and has the potential to achieve order-of-magnitude improvement in energy efficiency while maintaining high performance. However, the lack of large-scale high-speed silicon demonstrations and inductor area overheads are two major concerns. This dissertation focuses on scalable charge-recovery designs. We present a semi-automated design flow to enable the design of large-scale charge-recovery chips. We also present a new architecture that uses in-package inductors, eliminating the area overheads caused by the use of integrated inductors in high-performance charge-recovery chips. To demonstrate our semi-automated flow, which uses custom-designed standard-cell-like dynamic cells, we have designed a 576-bit charge-recovery low-density parity-check (LDPC) decoder chip. Functioning correctly at clock speeds above 1 GHz, this prototype is the first-ever demonstration of a GHz-speed charge-recovery chip of significant complexity. In terms of energy consumption, this chip improves over recent state-of-the-art LDPCs by at least 1.3 times with comparable or better area efficiency. To demonstrate our architecture for eliminating inductor overheads, we have designed a charge-recovery LDPC decoder chip with in-package inductors. This test-chip has been fabricated in a 65nm CMOS flip-chip process. A custom 6-layer FC-BGA package substrate has been designed with 16 inductors embedded in the fifth layer of the package substrate, yielding higher Q and significantly improving area efficiency and energy efficiency compared to their on-chip counterparts. From measurements, this chip achieves at least 2.3 times lower energy consumption with better area efficiency over state-of-the-art published designs.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116653/1/terryou_1.pd

A Study of Deep Learning Robustness Against Computation Failures

For many types of integrated circuits, accepting larger failure rates in computations can be used to improve energy efficiency. We study the performance of faulty implementations of certain deep neural networks based on pessimistic and optimistic models of the effect of hardware faults. After identifying the impact of hyperparameters such as the number of layers on robustness, we study the ability of the network to compensate for computational failures through an increase of the network size. We show that some networks can achieve equivalent performance under faulty implementations, and quantify the required increase in computational complexity

Design tradeoffs and challenges in practical coherent optical transceiver implementations

This tutorial discusses the design and ASIC implementation of coherent optical transceivers. Algorithmic and architectural options and tradeoffs between performance and complexity/power dissipation are presented. Particular emphasis is placed on flexible (or reconfigurable) transceivers because of their importance as building blocks of software-defined optical networks. The paper elaborates on some advanced digital signal processing (DSP) techniques such as iterative decoding, which are likely to be applied in future coherent transceivers based on higher order modulations. Complexity and performance of critical DSP blocks such as the forward error correction decoder and the frequency-domain bulk chromatic dispersion equalizer are analyzed in detail. Other important ASIC implementation aspects including physical design, signal and power integrity, and design for testability, are also discussed.Fil: Morero, Damián Alfonso. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. ClariPhy Argentina S.A.; ArgentinaFil: Castrillon, Alejandro. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; ArgentinaFil: Aguirre, Alejandro. ClariPhy Argentina S.A.; ArgentinaFil: Hueda, Mario Rafael. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Estudios Avanzados en Ingeniería y Tecnología. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto de Estudios Avanzados en Ingeniería y Tecnología; ArgentinaFil: Agazzi, Oscar Ernesto. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. ClariPhy Argentina S.A.; Argentin

VLSI Implementation of Deep Neural Network Using Integral Stochastic Computing

The hardware implementation of deep neural networks (DNNs) has recently received tremendous attention: many applications in fact require high-speed operations that suit a hardware implementation. However, numerous elements and complex interconnections are usually required, leading to a large area occupation and copious power consumption. Stochastic computing has shown promising results for low-power area-efficient hardware implementations, even though existing stochastic algorithms require long streams that cause long latencies. In this paper, we propose an integer form of stochastic computation and introduce some elementary circuits. We then propose an efficient implementation of a DNN based on integral stochastic computing. The proposed architecture has been implemented on a Virtex7 FPGA, resulting in 45% and 62% average reductions in area and latency compared to the best reported architecture in literature. We also synthesize the circuits in a 65 nm CMOS technology and we show that the proposed integral stochastic architecture results in up to 21% reduction in energy consumption compared to the binary radix implementation at the same misclassification rate. Due to fault-tolerant nature of stochastic architectures, we also consider a quasi-synchronous implementation which yields 33% reduction in energy consumption w.r.t. the binary radix implementation without any compromise on performance.Comment: 11 pages, 12 figure

Low-Power and Error-Resilient VLSI Circuits and Systems.

Efficient low-power operation is critically important for the success of the next-generation signal processing applications. Device and supply voltage have been continuously scaled to meet a more constrained power envelope, but scaling has created resiliency challenges, including increasing timing faults and soft errors. Our research aims at designing low-power and robust circuits and systems for signal processing by drawing circuit, architecture, and algorithm approaches. To gain an insight into the system faults due to supply voltage reduction, we researched the two primary effects that determine the minimum supply voltage (VMIN) in Intel’s tri-gate CMOS technology, namely process variations and gate-dielectric soft breakdown. We determined that voltage scaling increases the timing window that sequential circuits are vulnerable. Thus, we proposed a new hold-time violation metric to define hold-time VMIN, which has been adopted as a new design standard. Device scaling increases soft errors which affect circuit reliability. Through extensive soft error characterization using two 65nm CMOS test chips, we studied the soft error mechanisms and its dependence on supply voltage and clock frequency. This study laid the foundation of the first 65nm DSP chip design for a NASA spaceflight project. To mitigate such random errors, we proposed a new confidence-driven architecture that effectively enhances the error resiliency of deeply scaled CMOS and post-CMOS circuits. Designing low-power resilient systems can effectively leverage application-specific algorithmic approaches. To explore design opportunities in the algorithmic domain, we demonstrate an application-specific detection and decoding processor for multiple-input multiple-output (MIMO) wireless communication. To enhance the receive error rate for a robust wireless communication, we designed a joint detection and decoding technique by enclosing detection and decoding in an iterative loop to enhance both interference cancellation and error reduction. A proof-of-concept chip design was fabricated for the next-generation 4x4 256QAM MIMO systems. Through algorithm-architecture optimizations and low-power circuit techniques, our design achieves significant improvements in throughput, energy efficiency and error rate, paving the way for future developments in this area.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/110323/1/uchchen_1.pd