Spin-Based Neuron Model with Domain Wall Magnets as Synapse

We present artificial neural network design using spin devices that achieves ultra low voltage operation, low power consumption, high speed, and high integration density. We employ spin torque switched nano-magnets for modelling neuron and domain wall magnets for compact, programmable synapses. The spin based neuron-synapse units operate locally at ultra low supply voltage of 30mV resulting in low computation power. CMOS based inter-neuron communication is employed to realize network-level functionality. We corroborate circuit operation with physics based models developed for the spin devices. Simulation results for character recognition as a benchmark application shows 95% lower power consumption as compared to 45nm CMOS design

Neuro-fuzzy chip to handle complex tasks with analog performance

This paper presents a mixed-signal neuro-fuzzy controller chip which, in terms of power consumption, input–output delay, and precision, performs as a fully analog implementation. However, it has much larger complexity than its purely analog counterparts. This combination of performance and complexity is achieved through the use of a mixed-signal architecture consisting of a programmable analog core of reduced complexity, and a strategy, and the associated mixed-signal circuitry, to cover the whole input space through the dynamic programming of this core. Since errors and delays are proportional to the reduced number of fuzzy rules included in the analog core, they are much smaller than in the case where the whole rule set is implemented by analog circuitry. Also, the area and the power consumption of the new architecture are smaller than those of its purely analog counterparts simply because most rules are implemented through programming. The Paper presents a set of building blocks associated to this architecture, and gives results for an exemplary prototype. This prototype, called multiplexing fuzzy controller (MFCON), has been realized in a CMOS 0.7 um standard technology. It has two inputs, implements 64 rules, and features 500 ns of input to output delay with 16-mW of power consumption. Results from the chip in a control application with a dc motor are also provided

Neuro-fuzzy chip to handle complex tasks with analog performance

This Paper presents a mixed-signal neuro-fuzzy controller chip which, in terms of power consumption, input-output delay and precision performs as a fully analog implementation. However, it has much larger complexity than its purely analog counterparts. This combination of performance and complexity is achieved through the use of a mixed-signal architecture consisting of a programmable analog core of reduced complexity, and a strategy, and the associated mixed-signal circuitry, to cover the whole input space through the dynamic programming of this core [1]. Since errors and delays are proportional to the reduced number of fuzzy rules included in the analog core, they are much smaller than in the case where the whole rule set is implemented by analog circuitry. Also, the area and the power consumption of the new architecture are smaller than those of its purely analog counterparts simply because most rules are implemented through programming. The Paper presents a set of building blocks associated to this architecture, and gives results for an exemplary prototype. This prototype, called MFCON, has been realized in a CMOS 0.7μm standard technology. It has two inputs, implements 64 rules and features 500ns of input to output delay with 16mW of power consumption. Results from the chip in a control application with a DC motor are also provided

A versatile sensor interface for programmable vision systems-on-chip

This paper describes an optical sensor interface designed for a programmable mixed-signal vision chip. This chip has been designed and manufactured in a standard 0.35μm n-well CMOS technology with one poly layer and five metal layers. It contains a digital shell for control and data interchange, and a central array of 128 × 128 identical cells, each cell corresponding to a pixel. Die size is 11.885 × 12.230mm2 and cell size is 75.7μm × 73.3μm. Each cell contains 198 transistors dedicated to functions like processing, storage, and sensing. The system is oriented to real-time, single-chip image acquisition and processing. Since each pixel performs the basic functions of sensing, processing and storage, data transferences are fully parallel (image-wide). The programmability of the processing functions enables the realization of complex image processing functions based on the sequential application of simpler operations. This paper provides a general overview of the system architecture and functionality, with special emphasis on the optical interface.European Commission IST-1999-19007Office of Naval Research (USA) N00014021088

Realization of a ROIC for 72x4 PV-IR detectors

Silicon Readout Integrated Circuits (ROIC) for HgCdTe Focal Plane Arrays of 1x4 and 72x4 photovoltaic detectors are represented. The analog circuit blocks are completely identical for both, while the digital control circuit is modified to take into account the larger array size. The manufacturing technology is 0.35μm, double poly-Si, three-metal CMOS process. ROIC structure includes four elements TDI functioning with a super sampling rate of 3, bidirectional scanning, dead pixel de-selection, automatic gain adjustment in response to pixel deselection besides programmable four gain setting (up to 2.58pC storage), and programmable integration time. ROIC has four outputs with a dynamic range of 2.8V (from 1.2V to 4V) for an output load of 10pF capacitive in parallel with 1MΩ resistance, and operates at a clock frequency of 5 MHz. The input referred noise is less than 1037 μV with 460 fF integration capacitor, corresponding to 2978 electrons

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

Design of a ROIC for scanning type HgCdTe LWIR focal plane arrays

Design of a silicon readout integrated circuit (ROIC) for LWIR HgCdTe Focal Plane is presented. ROIC incorporates time delay integration (TDI) functionality over seven elements with a supersampling rate of three, increasing SNR and the spatial resolution. Novelty of this topology is inside TDI stage; integration of charges in TDI stage implemented in current domain by using switched current structures that reduces required area for chip and improves linearity performance. ROIC, in terms of functionality, is capable of bidirectional scan, programmable integration time and 5 gain settings at the input. Programming can be done parallel or serially with digital interface. ROIC can handle up to 3.5V dynamic range with the input stage to be direct injection (DI) type. With the load being 10pF capacitive in parallel with 1MΩ resistance, output settling time is less than 250nsec enabling the clock frequency up to 4MHz. The manufacturing technology is 0.35μm, double poly-Si, four-metal (3 metals and 1 top metal) 5V CMOS process