Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective

On metrics of density and power efficiency, neuromorphic technologies have the potential to surpass mainstream computing technologies in tasks where real-time functionality, adaptability, and autonomy are essential. While algorithmic advances in neuromorphic computing are proceeding successfully, the potential of memristors to improve neuromorphic computing have not yet born fruit, primarily because they are often used as a drop-in replacement to conventional memory. However, interdisciplinary approaches anchored in machine learning theory suggest that multifactor plasticity rules matching neural and synaptic dynamics to the device capabilities can take better advantage of memristor dynamics and its stochasticity. Furthermore, such plasticity rules generally show much higher performance than that of classical Spike Time Dependent Plasticity (STDP) rules. This chapter reviews the recent development in learning with spiking neural network models and their possible implementation with memristor-based hardware

Integer Echo State Networks: Hyperdimensional Reservoir Computing

We propose an approximation of Echo State Networks (ESN) that can be efficiently implemented on digital hardware based on the mathematics of hyperdimensional computing. The reservoir of the proposed Integer Echo State Network (intESN) is a vector containing only n-bits integers (where n<8 is normally sufficient for a satisfactory performance). The recurrent matrix multiplication is replaced with an efficient cyclic shift operation. The intESN architecture is verified with typical tasks in reservoir computing: memorizing of a sequence of inputs; classifying time-series; learning dynamic processes. Such an architecture results in dramatic improvements in memory footprint and computational efficiency, with minimal performance loss.Comment: 10 pages, 10 figures, 1 tabl

Computing the entire area/power consumption versus delay tradeoff curve for gate sizing with a piecewise linear simulator

The gate sizing problem is the problem of finding load drive capabilities for all gates in a given Boolean network such, that a given delay limit is kept, and the necessary cost in terms of active area usage and/or power consumption is minimal. This paper describes a way to obtain the entire cost versus delay tradeoff curve of a combinational logic circuit in an efficient way. Every point on the resulting curve is the global optimum of the corresponding gate sizing problem. The problem is solved by mapping it onto piecewise linear models in such a way, that a piecewise linear (circuit) simulator can do the job. It is shown that this setup is very efficient, and can produce tradeoff curves for large circuits (thousands of gates) in a few minutes. Benchmark results for the entire set of MCNC '91 two-level examples are give

Static noise margin analysis for CMOS logic cells in near-threshold

The advancement of semiconductor technology enabled the fabrication of devices with faster switching activity and chips with higher integration density. However, these advances are facing new impediments related to energy and power dissipation. Besides, the increasing demand for portable devices leads the circuit design paradigm to prioritize energy efficiency instead of performance. Altogether, this scenario motivates engineers towards reducing the supply voltage to the near and subthreshold regime to increase the lifespan of battery-powered devices. Even though operating in these regime offer interesting energy-frequency trade-offs, it brings challenges concerning noise tolerance. As the supply voltage reduces, the available noise margins decrease, and circuits become more prone to functional failures. In addition, near and subthreshold circuits are more susceptible to manufacturing variability, hence further aggravating noise issues. Other issues, such as wire minimization and gate fan-out, also contribute to the relevance of evaluating the noise margin of circuits early in the design Accordingly, this work investigates how to improve the static noise margin of digital synchronous circuits that will operate at the near/subthreshold regime. This investigation produces a set of three original contributions. The first is an automated tool to estimate the static noise margin of CMOS combinational cells. The second contribution is a realistic static noise margin estimation methodology that considers process-voltage-temperature variations. Results show that the proposed methodology allows to reduce up to 70% of the static noise margin pessimism. Finally, the third contribution is the noise-aware cell design methodology and the inclusion of a noise evaluation of complex circuits during the logic synthesis. The resulting library achieved higher static noise margin (up to 24%) and less spread among different cells (up to 62%).Os avanços na tecnologia de semicondutores possibilitou que se fabricasse dispositivos com atividade de chaveamento mais rápida e com maior capacidade de integração de transistores. Estes avanços, todavia, impuseram novos empecilhos relacionados com a dissipação de potência e energia. Além disso, a crescente demanda por dispositivos portáteis levaram à uma mudança no paradigma de projeto de circuitos para que se priorize energia ao invés de desempenho. Este cenário motivou à reduzir a tensão de alimentação com qual os dispositivos operam para um regime próximo ou abaixo da tensão de limiar, com o objetivo de aumentar sua duração de bateria. Apesar desta abordagem balancear características de performance e energia, ela traz novos desafios com relação a tolerância à ruído. Ao reduzirmos a tensão de alimentação, também reduz-se a margem de ruído disponível e, assim, os circuitos tornam-se mais suscetíveis à falhas funcionais. Somado à este efeito, circuitos com tensões de alimentação nestes regimes são mais sensíveis à variações do processo de fabricação, logo agravando problemas com ruído. Existem também outros aspectos, tais como a miniaturização das interconexões e a relação de fan-out de uma célula digital, que incentivam a avaliação de ruído nas fases iniciais do projeto de circuitos integrados Por estes motivos, este trabalho investiga como aprimorar a margem de ruído estática de circuitos síncronos digitais que irão operar em tensões no regime de tensão próximo ou abaixo do limiar. Esta investigação produz um conjunto de três contribuições originais. A primeira é uma ferramenta capaz de avaliar automaticamente a margem de ruído estática de células CMOS combinacionais. A segunda contribuição é uma metodologia realista para estimar a margem de ruído estática considerando variações de processo, tensão e temperatura. Os resultados obtidos mostram que a metodologia proposta permitiu reduzir até 70% do pessimismo das margens de ruído estática, Por último, a terceira contribuição é um fluxo de projeto de células combinacionais digitais considerando ruído, e uma abordagem para avaliar a margem de ruído estática de circuitos complexos durante a etapa de síntese lógica. A biblioteca de células resultante deste fluxo obteve maior margem de ruído (até 24%) e menor variação entre diferentes células (até 62%)

Energy consumption in networks on chip : efficiency and scaling

Computer architecture design is in a new era where performance is increased by replicating processing cores on a chip rather than making CPUs larger and faster. This design strategy is motivated by the superior energy efficiency of the multi-core architecture compared to the traditional monolithic CPU. If the trend continues as expected, the number of cores on a chip is predicted to grow exponentially over time as the density of transistors on a die increases. A major challenge to the efficiency of multi-core chips is the energy used for communication among cores over a Network on Chip (NoC). As the number of cores increases, this energy also increases, imposing serious constraints on design and performance of both applications and architectures. Therefore, understanding the impact of different design choices on NoC power and energy consumption is crucial to the success of the multi- and many-core designs. This dissertation proposes methods for modeling and optimizing energy consumption in multi- and many-core chips, with special focus on the energy used for communication on the NoC. We present a number of tools and models to optimize energy consumption and model its scaling behavior as the number of cores increases. We use synthetic traffic patterns and full system simulations to test and validate our methods. Finally, we take a step back and look at the evolution of computer hardware in the last 40 years and, using a scaling theory from biology, present a predictive theory for power-performance scaling in microprocessor systems