Algorithm/Architecture Co-Design for Low-Power Neuromorphic Computing

The development of computing systems based on the conventional von Neumann architecture has slowed down in the past decade as complementary metal-oxide-semiconductor (CMOS) technology scaling becomes more and more difficult. To satisfy the ever-increasing demands in computing power, neuromorphic computing has emerged as an attractive alternative. This dissertation focuses on developing learning algorithm, hardware architecture, circuit components, and design methodologies for low-power neuromorphic computing that can be employed in various energy-constrained applications. A top-down approach is adopted in this research. Starting from the algorithm-architecture co-design, a hardware-friendly learning algorithm is developed for spiking neural networks (SNNs). The possibility of estimating gradients from spike timings is explored. The learning algorithm is developed for the ease of hardware implementation, as well as the compatibility with many well-established learning techniques developed for classic artificial neural networks (ANNs). An SNN hardware equipped with the proposed on-chip learning algorithm is implemented in CMOS technology. In this design, two unique features of SNNs, the event-driven computation and the inferring with a progressive precision, are leveraged to reduce the energy consumption. In addition to low-power SNN hardware, accelerators for ANNs are also presented to accelerate the adaptive dynamic programing algorithm. An efficient and flexible single-instruction-multiple-data architecture is proposed to exploit the inherent data-level parallelism in the inference and learning of ANNs. In addition, the accelerator is augmented with a virtual update technique, which helps improve the throughput and energy efficiency remarkably. Lastly, two techniques in the architecture-circuit level are introduced to mitigate the degraded reliability of the memory system in a neuromorphic hardware owing to the aggressively-scaled supply voltage and integration density. The first method uses on-chip feedback to compensate for the process variation and the second technique improves the throughput and energy efficiency of a conventional error-correction method.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144149/1/zhengn_1.pd

Dual Column, Replica Bitline Delay Technique Using Stochastic Current Processing for a Process Variation Tolerant, Low Power SRAM

SRAM (Static Random Access Memory) design has become the critical and important block in processing ICs with the highest bandwidth power rationed memories taking the business lead. As industry attempts to maintain Moore's law by shrinking the device size, we are facing greater issues with the variability due to random doping fluctuation in devices. This variation compels engineers to design for worst case conditions which leads to inefficient memory model, which make it difficult to stand in the business race. However, a smart design can lead to less variation and “exact” memory parametric prediction to achieve high performance, low power and maximum yield designs. Since, random variation today is more dominant, we consider the application of the central limit theorem to control memory read timing across PVT (Process Voltage Temperature) corners. A statistical read timing is developed for a SRAM memory bank. In the thesis two dummy columns, each at extreme end of the memory bank, are used to implement the statistical memory bank model. By combining Monte-Carlo analysis using cadence virtuoso, and PDK data for the CMOS process (IBM 7RF), an analytically memory timing model is verified. Our major goal is to improve yield across all memory banks in all die across all the wafers; slow-slow (SS), typical-typical (TT) and fast-fast (FF).A smart stochastic/statistical approach is used in the thesis to predict exact parametric yield parameters with less variation to design accurate memory system which gives high performance, low power and maximum yield across all PVT corners to keep you ahead in the memory business. The memory design is compared to the conventional self-timed replica architecture using coefficient of variance of a reference current generated using dummy column. The proposed architecture was able to achieve 62 percent across the process improved accuracy in reference current and sense amplifier firing variation. Proposed architecture looks promising for future node technologies where statistical variability and its impact in subthreshold region is more dominant.Electrical Engineerin

Development of the readout electronics for the high luminosity upgrade of the CMS outer strip tracker

The High-luminosity upgrade of the LHC will deliver the dramatic increase in luminosity required for precision measurements and to probe Beyond the Standard Model theories. At the same time, it will present unprecedented challenges in terms of pileup and radiation degradation. The CMS experiment is set for an extensive upgrade campaign, which includes the replacement of the current Tracker with another all-silicon detector with improved performance and reduced mass. One of the most ambitious aspects of the future Tracker will be the ability to identify high transverse momentum track candidates at every bunch crossing and with very low latency, in order to include tracking information at the L1 hardware trigger stage, a critical and effective step to achieve triggers with high purity and low threshold. This thesis presents the development and the testing of the CMS Binary Chip 2 (CBC2), a prototype Application Specific Integrated Circuit (ASIC) for the binary front-end readout of silicon strip detectors modules in the Outer Tracker, which also integrates the logic necessary to identify high transverse momentum candidates by correlating hits from two silicon strip detectors, separated by a few millimetres. The design exploits the relation between the transverse momentum and the curvature in the trajectory of charged particles subject to the large magnetic field of CMS. The logic which follows the analogue amplification and binary conversion rejects clusters wider than a programmable maximum number of adjacent strips, compensates for the geometrical offset in the alignment of the module, and correlates the hits between the two sensor layers. Data are stored in a memory buffer before being transferred to an additional buffer stage and being serially read-out upon receipt of a Level 1 trigger. The CBC2 has been subject to extensive testing since its production in January 2013: this work reports the results of electrical characterization, of the total ionizing dose irradiation tests, and the performance of a prototype module instrumented with CBC2 in realistic conditions in a beam test. The latter is the first experimental demonstration of the Pt-selection principle central to the future of CMS. Several total-ionizing-dose tests highlighted no functional issue, but observed significant excess static current for doses <1 Mrad. The source of the excess was traced to static leakage current in the memory pipeline, and is believed to be a consequence of the high instantaneous dose delivered by the x-ray setup. Nevertheless, a new SRAM layout aimed at removing the leakage path was proposed for the CBC3. The results of single event upset testing of the chip are also reported, two of the three distinct memory circuits used in the chip were proven to meet the expected robustness, while the third will be replaced in the next iteration of the chip. Finally, the next version of the ASIC is presented, highlighting the additional features of the final prototype, such as half-strip resolution, additional trigger logic functionality, longer trigger latency and higher rate, and fully synchronous stub readout.Open Acces

Simulation of charge-trapping in nano-scale MOSFETs in the presence of random-dopants-induced variability

The growing variability of electrical characteristics is a major issue associated with continuous downscaling of contemporary bulk MOSFETs. In addition, the operating conditions brought about by these same scaling trends have pushed MOSFET degradation mechanisms such as Bias Temperature Instability (BTI) to the forefront as a critical reliability threat. This thesis investigates the impact of this ageing phenomena, in conjunction with device variability, on key MOSFET electrical parameters. A three-dimensional drift-diffusion approximation is adopted as the simulation approach in this work, with random dopant fluctuations—the dominant source of statistical variability—included in the simulations. The testbed device is a realistic 35 nm physical gate length n-channel conventional bulk MOSFET. 1000 microscopically different implementations of the transistor are simulated and subjected to charge-trapping at the oxide interface. The statistical simulations reveal relatively rare but very large threshold voltage shifts, with magnitudes over 3 times than that predicted by the conventional theoretical approach. The physical origin of this effect is investigated in terms of the electrostatic influences of the random dopants and trapped charges on the channel electron concentration. Simulations with progressively increased trapped charge densities—emulating the characteristic condition of BTI degradation—result in further variability of the threshold voltage distribution. Weak correlations of the order of 10-2 are found between the pre-degradation threshold voltage and post-degradation threshold voltage shift distributions. The importance of accounting for random dopant fluctuations in the simulations is emphasised in order to obtain qualitative agreement between simulation results and published experimental measurements. Finally, the information gained from these device-level physical simulations is integrated into statistical compact models, making the information available to circuit designers

Circuit Design

Circuit Design = Science + Art! Designers need a skilled "gut feeling" about circuits and related analytical techniques, plus creativity, to solve all problems and to adhere to the specifications, the written and the unwritten ones. You must anticipate a large number of influences, like temperature effects, supply voltages changes, offset voltages, layout parasitics, and numerous kinds of technology variations to end up with a circuit that works. This is challenging for analog, custom-digital, mixed-signal or RF circuits, and often researching new design methods in relevant journals, conference proceedings and design tools unfortunately gives the impression that just a "wild bunch" of "advanced techniques" exist. On the other hand, state-of-the-art tools nowadays indeed offer a good cockpit to steer the design flow, which include clever statistical methods and optimization techniques.Actually, this almost presents a second breakthrough, like the introduction of circuit simulators 40 years ago! Users can now conveniently analyse all the problems (discover, quantify, verify), and even exploit them, for example for optimization purposes. Most designers are caught up on everyday problems, so we fit that "wild bunch" into a systematic approach for variation-aware design, a designer's field guide and more. That is where this book can help! Circuit Design: Anticipate, Analyze, Exploit Variations starts with best-practise manual methods and links them tightly to up-to-date automation algorithms. We provide many tractable examples and explain key techniques you have to know. We then enable you to select and setup suitable methods for each design task - knowing their prerequisites, advantages and, as too often overlooked, their limitations as well. The good thing with computers is that you yourself can often verify amazing things with little effort, and you can use software not only to your direct advantage in solving a specific problem, but also for becoming a better skilled, more experienced engineer. Unfortunately, EDA design environments are not good at all to learn about advanced numerics. So with this book we also provide two apps for learning about statistic and optimization directly with circuit-related examples, and in real-time so without the long simulation times. This helps to develop a healthy statistical gut feeling for circuit design. The book is written for engineers, students in engineering and CAD / methodology experts. Readers should have some background in standard design techniques like entering a design in a schematic capture and simulating it, and also know about major technology aspects

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

Circuit Design

Circuit Design = Science + Art! Designers need a skilled "gut feeling" about circuits and related analytical techniques, plus creativity, to solve all problems and to adhere to the specifications, the written and the unwritten ones. You must anticipate a large number of influences, like temperature effects, supply voltages changes, offset voltages, layout parasitics, and numerous kinds of technology variations to end up with a circuit that works. This is challenging for analog, custom-digital, mixed-signal or RF circuits, and often researching new design methods in relevant journals, conference proceedings and design tools unfortunately gives the impression that just a "wild bunch" of "advanced techniques" exist. On the other hand, state-of-the-art tools nowadays indeed offer a good cockpit to steer the design flow, which include clever statistical methods and optimization techniques.Actually, this almost presents a second breakthrough, like the introduction of circuit simulators 40 years ago! Users can now conveniently analyse all the problems (discover, quantify, verify), and even exploit them, for example for optimization purposes. Most designers are caught up on everyday problems, so we fit that "wild bunch" into a systematic approach for variation-aware design, a designer's field guide and more. That is where this book can help! Circuit Design: Anticipate, Analyze, Exploit Variations starts with best-practise manual methods and links them tightly to up-to-date automation algorithms. We provide many tractable examples and explain key techniques you have to know. We then enable you to select and setup suitable methods for each design task - knowing their prerequisites, advantages and, as too often overlooked, their limitations as well. The good thing with computers is that you yourself can often verify amazing things with little effort, and you can use software not only to your direct advantage in solving a specific problem, but also for becoming a better skilled, more experienced engineer. Unfortunately, EDA design environments are not good at all to learn about advanced numerics. So with this book we also provide two apps for learning about statistic and optimization directly with circuit-related examples, and in real-time so without the long simulation times. This helps to develop a healthy statistical gut feeling for circuit design. The book is written for engineers, students in engineering and CAD / methodology experts. Readers should have some background in standard design techniques like entering a design in a schematic capture and simulating it, and also know about major technology aspects

Characterisation and modelling of Random Telegraph Noise in nanometre devices

The power consumption of digital circuits is proportional to the square of operation voltage and the demand for low power circuits reduces the operation voltage towards the threshold of MOSFETs. A weak voltage signal makes circuits vulnerable to noise and the optimization of circuit design requires an accurate noise model. RTN is the dominant noise for modern CMOS technologies. This research focuses on the instability induced by Random Telegraph Noise (RTN) in nano-devices for low power applications, such as the Internet of Things (IoT). RTN is a stochastic noise that can be observed in the drain/gate current of a device when traps capture and emit electrons or holes. The impact of RTN instabilities in devices has been widely investigated. Although progress has been made, the understanding of RTN instabilities remains incomplete and many issues are unresolved. This work focuses on developing a statistical model for characterising, modelling and analysing of the impact of RTN on MOSFET performance, as well as to study the prediction for long-term RTN impact on real circuits. As transistor sizes are downscaled, a single trapped charge has a larger impact and RTN becomes increasingly important. To optimize circuit design, one needs to assess the impact of RTN on circuits, which can only be accomplished if there is an accurate statistical model of RTN. The dynamic Monte Carlo modelling requires the statistical distribution functions of both the amplitude and the capture/emission time (CET) of traps. Early works were focused on the amplitude distribution and the experimental data of CETs has been too limited to establish their statistical distribution reliably. In particular, the time window used has often been small, e.g. 10 sec or less, so that there is little data on slow traps. It is not known whether the CET distribution extracted from such a limited time window can be used to predict the RTN beyond the test time window. The first contribution of this work is three-fold: to provide long-term RTN data and use it to test the CET distributions proposed by early works; to propose a methodology for characterising the CET distribution for a fabrication process efficiently; and, for the first time, to verify the long-term prediction capability of a CET distribution beyond the time window used for its extraction. On the statistical distributions of RTN amplitude, three different distributions were proposed by early works: Lognormal, Exponential, and Gumbel distributions. They give substantially different RTN predictions and agreement has not been reached on which distribution should be used, calling the modelling accuracy into question. The second contribution of this work is to assess the accuracy of these three distributions and to explore other distributions for better accuracy. A novel criterion has been proposed for selecting distributions, which requires a monotonic reduction of modelling errors with increasing number of traps. The three existing distributions do not meet this criterion and thirteen other distributions are explored. It is found that the Generalized Extreme Value (GEV) distribution has the lowest error and meets the new criterion. Moreover, to reduce modelling errors, early works used bimodal Lognormal and Exponential distributions, which have more fitting parameters. Their errors, however, are still higher than those of the monomodal GEV distribution. GEV has a long distribution tail and predicts substantially worse RTN impact. The project highlights the uncertainty in predicting the RTN distribution tail by different statistical models. The last contribution of the project is studying the impact of different gate biases on RTN distributions. At two different gate voltage conditions: one close to threshold voltage |Vth| and the other under operating conditions, it is found that the RTN amplitude follows different distributions. At operating voltage condition, Lognormal distribution has the lowest error for RTN amplitude distribution in comparison with other distributions. The amplitude distribution at close to |Vth| has a longer tail compared with the distribution tail at operating voltage. However, RTN capture/emission time distribution is not impacted by gate bias and follows Log-uniform distribution

Accurate CMOS compact model and the corresponding circuit simulation in the presence of statistical variability and ageing

As CMOS scales down to sub-50 nm, it faces critical dimensions of charge and matter granularities, leading to the drastic increase of device parameter dispersion, named statistical variability, which is one of the main contemporary challenges for further downscaling and makes each device atomistically different leading to broad dispersion of their electrical characteristics. In addition, device reliability concerns gain inertia; among them Bias Temperature Instability (BTI) shortens device lifetime by trapping charges in defect states of the insulator or at the interface. The interplay between statistical variability and BTI results in more variations on device performance and thus greatly affect circuit performance. In turn design methodologies must evolve towards variability and reliability aware design. To do so statistical compact models including both the effects of statistical variability and BTI-induced ageing are required for the large-scale statistical circuit simulation of variability and reliability. In this study, the application of accurate compact models, that describe performance variation in the presence of both statistical variability and reliability at arbitrary BTI-induced ageing levels, to SRAM circuit simulation is described. Both SRAM cell stability and write performance are evaluated and it is seen that, due to the accurate description of device performance distributions provided by the compact models and the sensitivity of these SRAM performance metrics on device performance, the approach presented here is better suited to high-sigma statistical circuit analysis than conventional approaches based upon assumed Gaussian distributions. The approach is demonstrated using a 25 nm gate length bulk MOSFET whose performance variation is obtained from statistical TCAD simulation using the GSS simulator GARAND. The simulated performance data is then used directly as the target for BSIM4 compact model extraction that ensures device figures of merit are well resolved for each device in a statistical ensemble. The distribution of compact model parameters is then generalised into an algebraic form using Generalized Lambda Distribution (GLD) methods, so that a sufficiently large number of compact models can later be generated and interpolated at arbitrary ageing levels. Finally compact models generated in this way are used to evaluate SRAM write performance and stability under the influence of statistical variability and BTI-induced ageing

A review of advances in pixel detectors for experiments with high rate and radiation

The Large Hadron Collider (LHC) experiments ATLAS and CMS have established hybrid pixel detectors as the instrument of choice for particle tracking and vertexing in high rate and radiation environments, as they operate close to the LHC interaction points. With the High Luminosity-LHC upgrade now in sight, for which the tracking detectors will be completely replaced, new generations of pixel detectors are being devised. They have to address enormous challenges in terms of data throughput and radiation levels, ionizing and non-ionizing, that harm the sensing and readout parts of pixel detectors alike. Advances in microelectronics and microprocessing technologies now enable large scale detector designs with unprecedented performance in measurement precision (space and time), radiation hard sensors and readout chips, hybridization techniques, lightweight supports, and fully monolithic approaches to meet these challenges. This paper reviews the world-wide effort on these developments.Comment: 84 pages with 46 figures. Review article.For submission to Rep. Prog. Phy