A Compact CMOS Memristor Emulator Circuit and its Applications

Conceptual memristors have recently gathered wider interest due to their diverse application in non-von Neumann computing, machine learning, neuromorphic computing, and chaotic circuits. We introduce a compact CMOS circuit that emulates idealized memristor characteristics and can bridge the gap between concepts to chip-scale realization by transcending device challenges. The CMOS memristor circuit embodies a two-terminal variable resistor whose resistance is controlled by the voltage applied across its terminals. The memristor 'state' is held in a capacitor that controls the resistor value. This work presents the design and simulation of the memristor emulation circuit, and applies it to a memcomputing application of maze solving using analog parallelism. Furthermore, the memristor emulator circuit can be designed and fabricated using standard commercial CMOS technologies and opens doors to interesting applications in neuromorphic and machine learning circuits.Comment: Submitted to International Symposium of Circuits and Systems (ISCAS) 201

Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition

A neuromorphic chip that combines CMOS analog spiking neurons and memristive synapses offers a promising solution to brain-inspired computing, as it can provide massive neural network parallelism and density. Previous hybrid analog CMOS-memristor approaches required extensive CMOS circuitry for training, and thus eliminated most of the density advantages gained by the adoption of memristor synapses. Further, they used different waveforms for pre and post-synaptic spikes that added undesirable circuit overhead. Here we describe a hardware architecture that can feature a large number of memristor synapses to learn real-world patterns. We present a versatile CMOS neuron that combines integrate-and-fire behavior, drives passive memristors and implements competitive learning in a compact circuit module, and enables in-situ plasticity in the memristor synapses. We demonstrate handwritten-digits recognition using the proposed architecture using transistor-level circuit simulations. As the described neuromorphic architecture is homogeneous, it realizes a fundamental building block for large-scale energy-efficient brain-inspired silicon chips that could lead to next-generation cognitive computing.Comment: This is a preprint of an article accepted for publication in IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol 5, no. 2, June 201

A CMOS Spiking Neuron for Dense Memristor-Synapse Connectivity for Brain-Inspired Computing

Neuromorphic systems that densely integrate CMOS spiking neurons and nano-scale memristor synapses open a new avenue of brain-inspired computing. Existing silicon neurons have molded neural biophysical dynamics but are incompatible with memristor synapses, or used extra training circuitry thus eliminating much of the density advantages gained by using memristors, or were energy inefficient. Here we describe a novel CMOS spiking leaky integrate-and-fire neuron circuit. Building on a reconfigurable architecture with a single opamp, the described neuron accommodates a large number of memristor synapses, and enables online spike timing dependent plasticity (STDP) learning with optimized power consumption. Simulation results of an 180nm CMOS design showed 97% power efficiency metric when realizing STDP learning in 10,000 memristor synapses with a nominal 1M{\Omega} memristance, and only 13{\mu}A current consumption when integrating input spikes. Therefore, the described CMOS neuron contributes a generalized building block for large-scale brain-inspired neuromorphic systems.Comment: This is a preprint of an article accepted for publication in International Joint Conference on Neural Networks (IJCNN) 201

A CMOS Spiking Neuron for Brain-Inspired Neural Networks with Resistive Synapses and In-Situ Learning

Nanoscale resistive memories are expected to fuel dense integration of electronic synapses for large-scale neuromorphic system. To realize such a brain-inspired computing chip, a compact CMOS spiking neuron that performs in-situ learning and computing while driving a large number of resistive synapses is desired. This work presents a novel leaky integrate-and-fire neuron design which implements the dual-mode operation of current integration and synaptic drive, with a single opamp and enables in-situ learning with crossbar resistive synapses. The proposed design was implemented in a 0.18 $\mu$m CMOS technology. Measurements show neuron's ability to drive a thousand resistive synapses, and demonstrate an in-situ associative learning. The neuron circuit occupies a small area of 0.01 mm$^2$ and has an energy-efficiency of 9.3 pJ$/$spike$/$synapse

A Low-Power Single-Bit Continuous-Time ΔΣ Converter with 92.5 dB Dynamic Range for Biomedical Applications

A third-order single-bit CT-ΔΣ modulator for generic biomedical applications is implemented in a 0.15 µm FDSOI CMOS process. The overall power efficiency is attained by employing a single-bit ΔΣ and a subthreshold FDSOI process. The loop-filter coefficients are determined using a systematic design centering approach by accounting for the integrator non-idealities. The single-bit CT-ΔΣ modulator consumes 110 µW power from a 1.5 V power supply when clocked at 6.144 MHz. The simulation results for the modulator exhibit a dynamic range of 94.4 dB and peak SNDR of 92.4 dB for 6 kHz signal bandwidth. The figure of merit (FoM) for the third-order, single-bit CT-ΔΣ modulator is 0.271 pJ/level

Design of Wideband Continuous-Time ΔΣ ADCs Using Two-Step Quantizers

Continuous-time delta sigma (CT-ΔΣ) ADCs are established as the data conversion architecture of choice for the next-generation wireless applications. Several efforts have been made to simultaneously improve the bandwidth and dynamic range of ΔΣ ADCs. We proposed using two-step quantizer in a single-loop CT-ΔΣ modulator to achieve higher conversion bandwidth. This paper presents a tutorial for employing the design technique through a 130n CMOS implementation. The proposed 640 MS/s, 4th order continuous-time delta sigma modulator (CT-ΔΣM) incorporates a two-step 5-bit quantizer, consisting of only 13 comparators. The CT-ΔΣM achieves a dynamic range of 70 dB, peak SNDR of 65.3 dB with 32 MHz bandwidth (OSR = 10) while consuming only 30 mW from the 1.2 V supply. The relevant design trade offs have been discussed and presented with simulation results

A 1 GS/s, 31 MHz BW, 76.3 dB Dynamic Range, 34 mW CT-ΔΣ ADC with 1.5 Cycle Quantizer Delay and Improved STF

A 1 GS/s Continuous-time Delta-Sigma modulator (CT-ΔΣM) with 31 MHz bandwidth, 76.3 dB dynamic range and 72.5 dB signal-to-noise is reported in a 0.13μm CMOS technology. The design employs an excess loop delay (ELD) of more than one clock cycle for achieving higher sampling rate. The ELD is compensated using a fast-loop formed around the last integrator by using a sample-and-hold. Further, the effect of this ELD compensation scheme on the signal transfer function (STF) of a feedforward CT-ΔΣ architecture has been analyzed and reported. In this work, an improved STF is achieved by using a combination of feed-forward, feed-back and feed-in paths and power consumption is reduced by eliminating the adder opamp. This CT-ΔΣM has a conversion bandwidth of 31 MHz and consumes 34 mW from the 1.2V power supply. The relevant design trade-offs have been investigated and presented along with simulation results