Circuits and Systems Advances in Near Threshold Computing

Modern society is witnessing a sea change in ubiquitous computing, in which people have embraced computing systems as an indispensable part of day-to-day existence. Computation, storage, and communication abilities of smartphones, for example, have undergone monumental changes over the past decade. However, global emphasis on creating and sustaining green environments is leading to a rapid and ongoing proliferation of edge computing systems and applications. As a broad spectrum of healthcare, home, and transport applications shift to the edge of the network, near-threshold computing (NTC) is emerging as one of the promising low-power computing platforms. An NTC device sets its supply voltage close to its threshold voltage, dramatically reducing the energy consumption. Despite showing substantial promise in terms of energy efficiency, NTC is yet to see widescale commercial adoption. This is because circuits and systems operating with NTC suffer from several problems, including increased sensitivity to process variation, reliability problems, performance degradation, and security vulnerabilities, to name a few. To realize its potential, we need designs, techniques, and solutions to overcome these challenges associated with NTC circuits and systems. The readers of this book will be able to familiarize themselves with recent advances in electronics systems, focusing on near-threshold computing

Study and development of low power consumption SRAMs on 28 nm FD-SOI CMOS process

Since analog circuit designs in CMOS nanometer (< 90 nm) nodes can be substantially affected by manufacturing process variations, circuit performance becomes more challenging to achieve efficient solutions by using analytical models. Extensive simulations are thus commonly required to provide a high yield. On the other hand, due to the fact that the classical bulk MOS structure is reaching scaling limits (< 32 nm), alternative approaches are being developed as successors, such as fully depleted silicon-oninsulator (FD-SOI), Multigate MOSFET, FinFETs, among others, and new design techniques emerge by taking advantage of the improved features of these devices. This thesis focused on the development of analytical expressions for the major performance parameters of the SRAM cache implemented in 28 nm FD-SOI CMOS, mainly to explore the transistor dimensions at low computational cost, thereby producing efficient designs in terms of energy consumption, speed and yield. By taking advantage of both low computational cost and close agreement results of the developed models, in this thesis we were able to propose a non-traditional sizing procedure for the simple 6T-SRAM cell, that unlike the traditional thin-cell design, transistor lengths are used as a design variable in order to reduce the static leakage. The single-P-well (SPW) structure in combination with reverse-body-biasing (RBB) technique were used to achieve a better balance between P-type and N-type transistors. As a result, we developed a 128 kB SRAM cache, whose post-layout simulations show that the circuit consumes an average energy per operation of 0.604 pJ/word-access (64 I/O bits) at supply voltage of 0.45 V and operation frequency of 40 MHz. The total chip area of the 128 kB SRAM cache is 0.060 mm2 .O projeto de circuitos analogicos em processos nanométricos CMOS ( < 90 nm) per substancialmente afetado pelas variacões do processo de fabricacão, sendo cada vez mais desafiador para os projetistas alcançar soluções eficientes no desempenho dos circuitos mediante o uso de modelos analíticos. Simulacões extensas com alto custo com- putacional sao normalmente requeridas para providenciar um correto funcionamento do circuito. Por outro lado, devido ao fato que a estrutura bulk-CMOS esta alcançando seus limites de escala (< 32 nm), outros transistores foram desenvolvidos como sucessores, tais como o fully depleted silicon-on-insulator (FD-SOI), Multigate MOSFET, entre outros, surgindo novas tecnicas de projeto que utilizam as características aprimoradas destes dispositivos. Dessa forma, esta tese de doutorado se foca no desenvolvimento de modelos analíticos dos parametros mais importantes do cache SRAM implementado em processo CMOS FD-SOI de 28 nm, principalmente para explorar as dimensõoes dos transistores com baixo custo computacional, e assim produzir solucões eficientes em termos de consumo de energia, velocidade e rendimento. Aproveitando o baixo custo computacional e a alta concordância dos modelos analíticos, nesta tese fomos capazes de propor um dimensionamento nao tradicional para a célula de memória 6T-SRAM, em que diferentemente é do classico dimensionamento "thin-cell”, os comprimentos dos transistores são utilizados como variável de projeto com o fim de reduzir o consumo estático de corrente. A estrutura single-P-well (SPW), combinada com a técnica reverse-body-biasing (RBB) foram utilizadas para alcançar um melhor balanço entre as correntes específicas dos transistores do tipo P e N

Microarchitectural Low-Power Design Techniques for Embedded Microprocessors

With the omnipresence of embedded processing in all forms of electronics today, there is a strong trend towards wireless, battery-powered, portable embedded systems which have to operate under stringent energy constraints. Consequently, low power consumption and high energy efficiency have emerged as the two key criteria for embedded microprocessor design. In this thesis we present a range of microarchitectural low-power design techniques which enable the increase of performance for embedded microprocessors and/or the reduction of energy consumption, e.g., through voltage scaling. In the context of cryptographic applications, we explore the effectiveness of instruction set extensions (ISEs) for a range of different cryptographic hash functions (SHA-3 candidates) on a 16-bit microcontroller architecture (PIC24). Specifically, we demonstrate the effectiveness of light-weight ISEs based on lookup table integration and microcoded instructions using finite state machines for operand and address generation. On-node processing in autonomous wireless sensor node devices requires deeply embedded cores with extremely low power consumption. To address this need, we present TamaRISC, a custom-designed ISA with a corresponding ultra-low-power microarchitecture implementation. The TamaRISC architecture is employed in conjunction with an ISE and standard cell memories to design a sub-threshold capable processor system targeted at compressed sensing applications. We furthermore employ TamaRISC in a hybrid SIMD/MIMD multi-core architecture targeted at moderate to high processing requirements (> 1 MOPS). A range of different microarchitectural techniques for efficient memory organization are presented. Specifically, we introduce a configurable data memory mapping technique for private and shared access, as well as instruction broadcast together with synchronized code execution based on checkpointing. We then study an inherent suboptimality due to the worst-case design principle in synchronous circuits, and introduce the concept of dynamic timing margins. We show that dynamic timing margins exist in microprocessor circuits, and that these margins are to a large extent state-dependent and that they are correlated to the sequences of instruction types which are executed within the processor pipeline. To perform this analysis we propose a circuit/processor characterization flow and tool called dynamic timing analysis. Moreover, this flow is employed in order to devise a high-level instruction set simulation environment for impact-evaluation of timing errors on application performance. The presented approach improves the state of the art significantly in terms of simulation accuracy through the use of statistical fault injection. The dynamic timing margins in microprocessors are then systematically exploited for throughput improvements or energy reductions via our proposed instruction-based dynamic clock adjustment (DCA) technique. To this end, we introduce a 6-stage 32-bit microprocessor with cycle-by-cycle DCA. Besides a comprehensive design flow and simulation environment for evaluation of the DCA approach, we additionally present a silicon prototype of a DCA-enabled OpenRISC microarchitecture fabricated in 28 nm FD-SOI CMOS. The test chip includes a suitable clock generation unit which allows for cycle-by-cycle DCA over a wide range with fine granularity at frequencies exceeding 1 GHz. Measurement results of speedups and power reductions are provided

Ultra-Low Power IoT Smart Visual Sensing Devices for Always-ON Applications

This work presents the design of a Smart Ultra-Low Power visual sensor architecture that couples together an ultra-low power event-based image sensor with a parallel and power-optimized digital architecture for data processing. By means of mixed-signal circuits, the imager generates a stream of address events after the extraction and binarization of spatial gradients. When targeting monitoring applications, the sensing and processing energy costs can be reduced by two orders of magnitude thanks to either the mixed-signal imaging technology, the event-based data compression and the use of event-driven computing approaches. From a system-level point of view, a context-aware power management scheme is enabled by means of a power-optimized sensor peripheral block, that requests the processor activation only when a relevant information is detected within the focal plane of the imager. When targeting a smart visual node for triggering purpose, the event-driven approach brings a 10x power reduction with respect to other presented visual systems, while leading to comparable results in terms of detection accuracy. To further enhance the recognition capabilities of the smart camera system, this work introduces the concept of event-based binarized neural networks. By coupling together the theory of binarized neural networks and focal-plane processing, a 17.8% energy reduction is demonstrated on a real-world data classification with a performance drop of 3% with respect to a baseline system featuring commercial visual sensors and a Binary Neural Network engine. Moreover, if coupling the BNN engine with the event-driven triggering detection flow, the average power consumption can be as low as the sleep power of 0.3mW in case of infrequent events, which is 8x lower than a smart camera system featuring a commercial RGB imager

Temperature and process-aware performance monitoring and compensation for an ULP multi-core cluster in 28nm UTBB FD-SOI technology

Environmental temperature variations, as well as process variations, have a detrimental effect on performance and reliability of embedded systems implemented with deep-sub micron technologies. This sensitivity significantly increases in ultra-low-power (ULP) devices that operate in near-threshold, due to the magnification of process variations and to the strong thermal inversion that affects advanced technology nodes. Supporting an extended range of reverse and forward body-bias, UTBB FD-SOI technology provides a powerful knob to compensate for such variations. In this work we propose a methodology to efficiently compensate, at run-time, these variations. The proposed method exploits on-line performance measurements by means of Process Monitoring Blocks (PMBs) coupled with on-chip low-power Body Bias Generators. We characterize the response of the PMBs versus the maximum achievable frequency of the system, deriving a predictive model able to estimate such frequency with an error of 3%. We apply this model to compensate Temperature-induced performance variations, estimating the maximum frequency with an error of 7%; we eliminate the error by adding an appropriate body-bias margin resulting in a worst case global power consumption overhead of 5%. As further improvement, we generalize the methodology to compensate also process variations, obtaining an error of 28% on the estimated maximum performance and compensating this error with an overhead of 17% on the global power consumption