Designing Approximate Computing Circuits with Scalable and Systematic Data-Driven Techniques

Semiconductor feature size has been shrinking significantly in the past decades. This decreasing trend of feature size leads to faster processing speed as well as lower area and power consumption. Among these attributes, power consumption has emerged as the primary concern in the design of integrated circuits in recent years due to the rapid increasing demand of energy efficient Internet of Things (IoT) devices. As a result, low power design approaches for digital circuits have become of great attractive in the past few years. To this end, approximate computing in hardware design has emerged as a promising design technique. It provides design opportunities to improve timing and energy efficiency by relaxing computing quality. This technique is feasible because of the error-resiliency of many emerging resource-hungry computational applications such as multimedia processing and machine learning. Thus, it is reasonable to utilize this characteristic to trade an acceptable amount of computing quality for energy saving. In the literature, most prior works on approximate circuit design focus on using manual design strategies to redesign fundamental computational blocks such as adders and multipliers. However, the manual design techniques are not suitable for system level hardware due to much higher design complexity. In order to tackle this challenge, we focus on designing scalable, systematic and general design methodologies that are applicable on any circuits. In this paper, we present two novel approximate circuit design methods based on machine learning techniques. Both methods skip the complicated manual analysis steps and primarily look at the given input-error pattern to generate approximate circuits. Our first work presents a framework for designing compensation block, an essential component in many approximate circuits, based on feature selection. Our second work further extends and optimizes this framework and integrates data-driven consideration into the design. Several case studies on fixed-width multipliers and other approximate circuits are presented to demonstrate the effectiveness of the proposed design methods. The experimental results show that both of the proposed methods are able to automatically and efficiently design low-error approximate circuits

Harnessing resilience: biased voltage overscaling for probabilistic signal processing

A central component of modern computing is the idea that computation requires determinism. Contrary to this belief, the primary contribution of this work shows that useful computation can be accomplished in an error-prone fashion. Focusing on low-power computing and the increasing push toward energy conservation, the work seeks to sacrifice accuracy in exchange for energy savings. Probabilistic computing forms the basis for this error-prone computation by diverging from the requirement of determinism and allowing for randomness within computing. Implemented as probabilistic CMOS (PCMOS), the approach realizes enormous energy sav- ings in applications that require probability at an algorithmic level. Extending probabilistic computing to applications that are inherently deterministic, the biased voltage overscaling (BIVOS) technique presented here constrains the randomness introduced through PCMOS. Doing so, BIVOS is able to limit the magnitude of any resulting deviations and realizes energy savings with minimal impact to application quality. Implemented for a ripple-carry adder, array multiplier, and finite-impulse-response (FIR) filter; a BIVOS solution substantially reduces energy consumption and does so with im- proved error rates compared to an energy equivalent reduced-precision solution. When applied to H.264 video decoding, a BIVOS solution is able to achieve a 33.9% reduction in energy consumption while maintaining a peak-signal-to-noise ratio of 35.0dB (compared to 14.3dB for a comparable reduced-precision solution). While the work presented here focuses on a specific technology, the technique realized through BIVOS has far broader implications. It is the departure from the conventional mindset that useful computation requires determinism that represents the primary innovation of this work. With applicability to emerging and yet to be discovered technologies, BIVOS has the potential to contribute to computing in a variety of fashions.PhDCommittee Chair: Anderson, David; Committee Member: Conte, Thomas; Committee Member: Ferri, Bonnie; Committee Member: Hasler, Paul; Committee Member: Mooney, Vincen

Delay Measurements and Self Characterisation on FPGAs

This thesis examines new timing measurement methods for self delay characterisation of Field-Programmable Gate Arrays (FPGAs) components and delay measurement of complex circuits on FPGAs. Two novel measurement techniques based on analysis of a circuit's output failure rate and transition probability is proposed for accurate, precise and efficient measurement of propagation delays. The transition probability based method is especially attractive, since it requires no modifications in the circuit-under-test and requires little hardware resources, making it an ideal method for physical delay analysis of FPGA circuits. The relentless advancements in process technology has led to smaller and denser transistors in integrated circuits. While FPGA users benefit from this in terms of increased hardware resources for more complex designs, the actual productivity with FPGA in terms of timing performance (operating frequency, latency and throughput) has lagged behind the potential improvements from the improved technology due to delay variability in FPGA components and the inaccuracy of timing models used in FPGA timing analysis. The ability to measure delay of any arbitrary circuit on FPGA offers many opportunities for on-chip characterisation and physical timing analysis, allowing delay variability to be accurately tracked and variation-aware optimisations to be developed, reducing the productivity gap observed in today's FPGA designs. The measurement techniques are developed into complete self measurement and characterisation platforms in this thesis, demonstrating their practical uses in actual FPGA hardware for cross-chip delay characterisation and accurate delay measurement of both complex combinatorial and sequential circuits, further reinforcing their positions in solving the delay variability problem in FPGAs

Implementation of arithmetic primitives using truly deep submicron technology (TDST)

The invention of the transistor in 1947 at Bell Laboratories revolutionised the electronics industry and created a powerful platform for emergence of new industries. The quest to increase the number of devices per chip over the last four decades has resulted in rapid transition from Small-Scale-Integration (SSI) and Large-Scale-lntegration (LSI), through to the Very-Large-Scale-Integration (VLSI) technologies, incorporating approximately 10 to 100 million devices per chip. The next phase in this evolution is the Ultra-Large-Scale-Integration (ULSI) aiming to realise new application domains currently not accessible to CMOS technology. Although technology is continuously evolving to produce smaller systems with minimised power dissipation, the IC industry is facing major challenges due to constraints on power density (W/cm2) and high dynamic (operating) and static (standby) power dissipation. Mobile multimedia communication and optical based technologies have rapidly become a significant area of research and development challenging a variety of technological fronts. The future emergence or 4G (4th Generation) wireless communications networks is further driving this development, requiring increasing levels of media rich content. The processing requirements for capture, conversion, compression, decompression, enhancement and display of higher quality multimedia, place heavy demands on current ULSI systems. This is also apparent for mobile applications and intelligent optical networks where silicon chip area and power dissipation become primary considerations. In addition to the requirements for very low power, compact size and real-time processing, the rapidly evolving nature of telecommunication networks means that flexible soft programmable systems capable of adaptation to support a number of different standards and/or roles become highly desirable. In order to fully realise the capabilities promised by the 4G and supporting intelligent networks, new enabling technologies arc needed to facilitate the next generation of personal communications devices. Most of the current solutions to meet these challenges are based on various implementations of conventional architectures. For decades, silicon has been the main platform of computing, however it is slow, bulky, runs too hot, and is too expensive. Thus, new approaches to architectures, driving multimedia and future telecommunications systems, are needed in order to extend the life cycle of silicon technology. The emergence of Truly Deep Submicron Technology (TDST) and related 3-D interconnection technologies have provided potential alternatives from conventional architectures to 3-D system solutions, through integration of IDST, Vertical Software Mapping and Intelligent Interconnect Technology (IIT). The concept of Soft-Chip Technology (SCT) entails integration of Soft• Processing Circuits with Soft-Configurable Circuits . This concept can effectively manipulate hardware primitives through vertical integration of control and data. Thus the notion of 3-D Soft-Chip emerges as a new design algorithm for content-rich multimedia, telecommunication and intelligent networking system applications. 3•D architectures (design algorithms used suitable for 3-D soft-chip technology), are driven by three factors. The first is development of new device technology (TDST) that can support new architectures with complexities of 100M to 1000M devices. The second is development of advanced wafer bonding techniques such as Indium bump and the more futuristic optical interconnects for 3-D soft-chip mapping. The third is related to improving the performance of silicon CMOS systems as devices continue to scale down in dimensions. One of the fundamental building blocks of any computer system is the arithmetic component. Optimum performance of the system is determined by the efficiency of each individual component, as well as the network as a whole entity. Development of configurable arithmetic primitives is the fundamental focus in 3-D architecture design where functionality can be implemented through soft configurable hardware elements. Therefore the ability to improve the performance capability of a system is of crucial importance for a successful design. Important factors that predict the efficiency of such arithmetic components are: • The propagation delay of the circuit, caused by the gate, diffusion and wire capacitances within !he circuit, minimised through transistor sizing. and • Power dissipation, which is generally based on node transition activity. [2] Although optimum performance of 3-D soft-chip systems is primarily established by the choice of basic primitives such as adders and multipliers, the interconnecting network also has significant degree of influence on !he efficiency of the system. 3-D superposition of devices can decrease interconnect delays by up to 60% compared to a similar planar architecture. This research is based on development and implementation of configurable arithmetic primitives, suitable to the 3-D architecture, and has these foci: • To develop a variety of arithmetic components such as adders and multipliers with particular emphasis on minimum area and compatible with 3-D soft-chip design paradigm. • To explore implementation of configurable distributed primitives for arithmetic processing. This entails optimisation of basic primitives, and using them as part of array processing. In this research the detailed designs of configurable arithmetic primitives are implemented using TDST O.l3µm (130nm) technology, utilising CAD software such as Mentor Graphics and Cadence in Custom design mode, carrying through design, simulation and verification steps

Variation-aware high-level DSP circuit design optimisation framework for FPGAs

The constant technology shrinking and the increasing demand for systems that operate under different power profiles with the maximum performance, have motivated the work in this thesis. Modern design tools that target FPGA devices take a conservative approach in the estimation of the maximum performance that can be achieved by a design when it is placed on a device, accounting for any variability in the fabrication process of the device. The work presented here takes a new view on the performance improvement of DSP designs by pushing them into the error-prone regime, as defined by the synthesis tools, and by investigating methodologies that reduce the impact of timing errors at the output of the system. In this work two novel error reduction techniques are proposed to address this problem. One is based on reduced-precision redundancy and the other on an error optimisation framework that uses information from a prior characterisation of the device. The first one is a generic architecture that is appended to existing arithmetic operators. The second defines the high-level parameters of the algorithm without using extra resources. Both of these methods allow to achieve graceful degradation whilst variation increases. A comparison of the new methods is laid against the existing methodologies, and conclusions drawn on the tradeoffs between their cost, in terms of resources and errors, and their benefits in terms of throughput. In some cases it is possible to double the performance of the design while still producing valid results.Open Acces