A Scalable 6-to-18 GHz Concurrent Dual-Band Quad-Beam Phased-Array Receiver in CMOS

This paper reports a 6-to-18 GHz integrated phased- array receiver implemented in 130-nm CMOS. The receiver is easily scalable to build a very large-scale phased-array system. It concurrently forms four independent beams at two different frequencies from 6 to 18 GHz. The nominal conversion gain of the receiver ranges from 16 to 24 dB over the entire band while the worst-case cross-band and cross-polarization rejections are achieved 48 dB and 63 dB, respectively. Phase shifting is performed in the LO path by a digital phase rotator with the worst-case RMS phase error and amplitude variation of 0.5° and 0.4 dB, respectively, over the entire band. A four-element phased-array receiver system is implemented based on four receiver chips. The measured array patterns agree well with the theoretical ones with a peak-to-null ratio of over 21.5 dB

Memory and information processing in neuromorphic systems

A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized. As Information and Communication Technologies continue to address the need for increased computational power through the increase of cores within a digital processor, neuromorphic engineers and scientists can complement this need by building processor architectures where memory is distributed with the processing. In this paper we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. These architectures range from serial clocked implementations of multi-neuron systems to massively parallel asynchronous ones and from purely digital systems to mixed analog/digital systems which implement more biological-like models of neurons and synapses together with a suite of adaptation and learning mechanisms analogous to the ones found in biological nervous systems. We describe the advantages of the different approaches being pursued and present the challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed neuromorphic computing platforms and system

Configurable Low Power Analog Multilayer Perceptron

A configurable, low power analog implementation of a multilayer perceptron (MLP) is presented in this work. It features a highly programmable system that allows the user to create a MLP neural network design of their choosing. In addition to the configurability, this neural network provides the ability of low power operation via analog circuitry in its neurons. The main MLP system is made up of 12 neurons that can be configurable to any number of layers and neurons per layer until all available resources are utilized. The MLP network is fabricated in a standard 0.13 μm CMOS process occupying approximately 1 mm2 of on-chip area. The MLP system is analyzed at several different configurations with all achieving a greater than 1 Tera-operations per second per Watt figure of merit. This work offers a high speed, low power, and scalable alternative to digital configurable neural networks

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

A Charge-Based CMOS Parallel Analog Vector Quantizer

We present an analog VLSI chip for parallel analog vector quantization. The MOSIS 2.0 μm double-poly CMOS Tiny chip contains an array of 16 x 16 charge-based distance estimation cells, implementing a mean absolute difference (MAD) metric operating on a 16-input analog vector field and 16 analog template vectors. The distance cell including dynamic template storage measures 60 x 78 μm^2. Additionally, the chip features a winner-take-all (WTA) output circuit of linear complexity, with global positive feedback for fast and decisive settling of a single winner output. Experimental results on the complete 16 x 16 VQ system demonstrate correct operation with 34 dB analog input dynamic range and 3 μsec cycle time at 0.7 mW power dissipation

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