Ultra-low Power Circuits and Architectures for Neuromorphic Computing Accelerators with Emerging TFETs and ReRAMs

Neuromorphic computing using post-CMOS technologies is gaining increasing popularity due to its promising potential to resolve the power constraints in Von-Neumann machine and its similarity to the operation of the real human brain. To design the ultra-low voltage and ultra-low power analog-to-digital converters (ADCs) for the neuromorphic computing systems, we explore advantages of tunnel field effect transistor (TFET) analog-to-digital converters (ADCs) on energy efficiency and temperature stability. A fully-differential SAR ADC is designed using 20 nm TFET technology with doubled input swing and controlled comparator input common-mode voltage. To further increase the resolution of the ADC, we design an energy efficient 12-bit noise shaping (NS) successive-approximation register (SAR) ADC. The 2nd-order noise shaping architecture with multiple feed-forward paths is adopted and analyzed to optimize system design parameters. By utilizing tunnel field effect transistors (TFETs), the Delta-Sigma SAR is realized under an ultra-low supply voltage VDD with high energy efficiency. The stochastic neuron is a key for event-based probabilistic neural networks. We propose a stochastic neuron using a metal-oxide resistive random-access memory (ReRAM). The ReRAM\u27s conducting filament with built-in stochasticity is used to mimic the neuron\u27s membrane capacitor, which temporally integrates input spikes. A capacitor-less neuron circuit is designed, laid out, and simulated. The output spiking train of the neuron obeys the Poisson distribution. Based on the ReRAM based neuron, we propose a scalable and reconfigurable architecture that exploits the ReRAM-based neurons for deep Spiking Neural Networks (SNNs). In prior publications, neurons were implemented using dedicated analog or digital circuits that are not area and energy efficient. In our work, for the first time, we address the scaling and power bottlenecks of neuromorphic architecture by utilizing a single one-transistor-one-ReRAM (1T1R) cell to emulate the neuron. We show that the ReRAM-based neurons can be integrated within the synaptic crossbar to build extremely dense Process Element (PE)–spiking neural network in memory array–with high throughput. We provide microarchitecture and circuit designs to enable the deep spiking neural network computing in memory with an insignificant area overhead

Energy efficient hybrid computing systems using spin devices

Emerging spin-devices like magnetic tunnel junctions (MTJ\u27s), spin-valves and domain wall magnets (DWM) have opened new avenues for spin-based logic design. This work explored potential computing applications which can exploit such devices for higher energy-efficiency and performance. The proposed applications involve hybrid design schemes, where charge-based devices supplement the spin-devices, to gain large benefits at the system level. As an example, lateral spin valves (LSV) involve switching of nanomagnets using spin-polarized current injection through a metallic channel such as Cu. Such spin-torque based devices possess several interesting properties that can be exploited for ultra-low power computation. Analog characteristic of spin current facilitate non-Boolean computation like majority evaluation that can be used to model a neuron. The magneto-metallic neurons can operate at ultra-low terminal voltage of ∼20mV, thereby resulting in small computation power. Moreover, since nano-magnets inherently act as memory elements, these devices can facilitate integration of logic and memory in interesting ways. The spin based neurons can be integrated with CMOS and other emerging devices leading to different classes of neuromorphic/non-Von-Neumann architectures. The spin-based designs involve `mixed-mode\u27 processing and hence can provide very compact and ultra-low energy solutions for complex computation blocks, both digital as well as analog. Such low-power, hybrid designs can be suitable for various data processing applications like cognitive computing, associative memory, and currentmode on-chip global interconnects. Simulation results for these applications based on device-circuit co-simulation framework predict more than ∼100x improvement in computation energy as compared to state of the art CMOS design, for optimal spin-device parameters

Data Conversion Within Energy Constrained Environments

Within scientific research, engineering, and consumer electronics, there is a multitude of new discrete sensor-interfaced devices. Maintaining high accuracy in signal quantization while staying within the strict power-budget of these devices is a very challenging problem. Traditional paths to solving this problem include researching more energy-efficient digital topologies as well as digital scaling.;This work offers an alternative path to lower-energy expenditure in the quantization stage --- content-dependent sampling of a signal. Instead of sampling at a constant rate, this work explores techniques which allow sampling based upon features of the signal itself through the use of application-dependent analog processing. This work presents an asynchronous sampling paradigm, based off the use of floating-gate-enabled analog circuitry. The basis of this work is developed through the mathematical models necessary for asynchronous sampling, as well the SPICE-compatible models necessary for simulating floating-gate enabled analog circuitry. These base techniques and circuitry are then extended to systems and applications utilizing novel analog-to-digital converter topologies capable of leveraging the non-constant sampling rates for significant sample and power savings

Ultra Low Energy Analog Image Processing Using Spin Neurons

In this work we present an ultra low energy, 'on-sensor' image processing architecture, based on cellular array of spin based neurons. The 'neuron' constitutes of a lateral spin valve (LSV) with multiple input magnets, connected to an output magnet, using metal channels. The low resistance, magneto-metallic neurons operate at a small terminal voltage of ~20mV, while performing analog computation upon photo sensor inputs. The static current-flow across the device terminals is limited to small periods, corresponding to magnet switching time, and, is determined by a low duty-cycle system-clock. Thus, the energy-cost of analog-mode processing, inevitable in most image sensing applications, is reduced and made comparable to that of dynamic and leakage power consumption in peripheral CMOS units. Performance of the proposed architecture for some common image sensing and processing applications like, feature extraction, halftone compression and digitization, have been obtained through physics based device simulation framework, coupled with SPICE. Results indicate that the proposed design scheme can achieve more than two orders of magnitude reduction in computation energy, as compared to the state of art CMOS designs, that are based on conventional mixed-signal image acquisition and processing schemes. To the best of authors' knowledge, this is the first work where application of nano magnets (in LSV's) in analog signal processing has been proposed

Digital and analog TFET circuits: Design and benchmark

In this work, we investigate by means of simulations the performance of basic digital, analog, and mixed-signal circuits employing tunnel-FETs (TFETs). The analysis reviews and complements our previous papers on these topics. By considering the same devices for all the analysis, we are able to draw consistent conclusions for a wide variety of circuits. A virtual complementary TFET technology consisting of III-V heterojunction nanowires is considered. Technology Computer Aided Design (TCAD) models are calibrated against the results of advanced full-quantum simulation tools and then used to generate look-up-tables suited for circuit simulations. The virtual complementary TFET technology is benchmarked against predictive technology models (PTM) of complementary silicon FinFETs for the 10 nm node over a wide range of supply voltages (VDD) in the sub-threshold voltage domain considering the same footprint between the vertical TFETs and the lateral FinFETs and the same static power. In spite of the asymmetry between p- and n-type transistors, the results show clear advantages of TFET technology over FinFET for VDDlower than 0.4 V. Moreover, we highlight how differences in the I-V characteristics of FinFETs and TFETs suggest to adapt the circuit topologies used to implement basic digital and analog blocks with respect to the most common CMOS solutions

Digital and analog TFET circuits: Design and benchmark

In this work, we investigate by means of simulations the performance of basic digital, analog, and mixed-signal circuits employing tunnel-FETs (TFETs). The analysis reviews and complements our previous papers on these topics. By considering the same devices for all the analysis, we are able to draw consistent conclusions for a wide variety of circuits. A virtual complementary TFET technology consisting of III-V heterojunction nanowires is considered. Technology Computer Aided Design (TCAD) models are calibrated against the results of advanced full-quantum simulation tools and then used to generate look-up-tables suited for circuit simulations. The virtual complementary TFET technology is benchmarked against predictive technology models (PTM) of complementary silicon FinFETs for the 10 nm node over a wide range of supply voltages (VDD) in the sub-threshold voltage domain considering the same footprint between the vertical TFETs and the lateral FinFETs and the same static power. In spite of the asymmetry between p- and n-type transistors, the results show clear advantages of TFET technology over FinFET for VDDlower than 0.4 V. Moreover, we highlight how differences in the I-V characteristics of FinFETs and TFETs suggest to adapt the circuit topologies used to implement basic digital and analog blocks with respect to the most common CMOS solutions

Low-Power Reconfigurable Sensing Circuitry for the Internet-of-Things Paradigm

With ubiquitous wireless communication via Wi-Fi and nascent 5th Generation mobile communications, more devices -- both smart and traditionally dumb -- will be interconnected than ever before. This burgeoning trend is referred to as the Internet-of-Things. These new sensing opportunities place a larger burden on the underlying circuitry that must operate on finite battery power and/or within energy-constrained environments. New developments of low-power reconfigurable analog sensing platforms like field-programmable analog arrays (FPAAs) present an attractive sensing solution by processing data in the analog domain while staying flexible in design. This work addresses some of the contemporary challenges of low-power wireless sensing via traditional application-specific sensing and with FPAAs. A large emphasis is placed on furthering the development of FPAAs by making them more accessible to designers without a strong integrated-circuit background -- much like FPGAs have done for digital designers

BOOLEAN AND BRAIN-INSPIRED COMPUTING USING SPIN-TRANSFER TORQUE DEVICES

Several completely new approaches (such as spintronic, carbon nanotube, graphene, TFETs, etc.) to information processing and data storage technologies are emerging to address the time frame beyond current Complementary Metal-Oxide-Semiconductor (CMOS) roadmap. The high speed magnetization switching of a nano-magnet due to current induced spin-transfer torque (STT) have been demonstrated in recent experiments. Such STT devices can be explored in compact, low power memory and logic design. In order to truly leverage STT devices based computing, researchers require a re-think of circuit, architecture, and computing model, since the STT devices are unlikely to be drop-in replacements for CMOS. The potential of STT devices based computing will be best realized by considering new computing models that are inherently suited to the characteristics of STT devices, and new applications that are enabled by their unique capabilities, thereby attaining performance that CMOS cannot achieve. The goal of this research is to conduct synergistic exploration in architecture, circuit and device levels for Boolean and brain-inspired computing using nanoscale STT devices. Specifically, we first show that the non-volatile STT devices can be used in designing configurable Boolean logic blocks. We propose a spin-memristor threshold logic (SMTL) gate design, where memristive cross-bar array is used to perform current mode summation of binary inputs and the low power current mode spintronic threshold device carries out the energy efficient threshold operation. Next, for brain-inspired computing, we have exploited different spin-transfer torque device structures that can implement the hard-limiting and soft-limiting artificial neuron transfer functions respectively. We apply such STT based neuron (or ‘spin-neuron’) in various neural network architectures, such as hierarchical temporal memory and feed-forward neural network, for performing “human-like” cognitive computing, which show more than two orders of lower energy consumption compared to state of the art CMOS implementation. Finally, we show the dynamics of injection locked Spin Hall Effect Spin-Torque Oscillator (SHE-STO) cluster can be exploited as a robust multi-dimensional distance metric for associative computing, image/ video analysis, etc. Our simulation results show that the proposed system architecture with injection locked SHE-STOs and the associated CMOS interface circuits can be suitable for robust and energy efficient associative computing and pattern matching

縦型ボディチャネルMOS Field-Effect Transistorを用いたマイクロプロセッサ向け高効率・高集積DC-DCコンバータとそのパワーマネジメントに関する研究

Tohoku University遠藤哲郎課