Improving reconfigurable systems reliability by combining periodical test and redundancy techniques: a case study

This paper revises and introduces to the field of reconfigurable computer systems, some traditional techniques used in the fields of fault-tolerance and testing of digital circuits. The target area is that of on-board spacecraft electronics, as this class of application is a good candidate for the use of reconfigurable computing technology. Fault tolerant strategies are used in order for the system to adapt itself to the severe conditions found in space. In addition, the paper describes some problems and possible solutions for the use of reconfigurable components, based on programmable logic, in space applications

Design of a 1.9 GHz low-power LFSR circuit using the Reed-Solomon algorithm for Pseudo-Random Test Pattern Generation

A linear feedback shift register (LFSR) has been frequently used in the Built-in Self-Test (BIST) designs for the pseudo-random test pattern generation. The volume of the test patterns and test power dissipation are the key features in the large complex designs. The objective of this paper is to propose a new LFSR circuit based on the proposed Reed-Solomon (RS) algorithm. The RS algorithm is created by considering the factors of the maximum length test pattern with a minimum distance over the time. Also, it has achieved an effective generation of test patterns over a stage of complexity order O (m log2 m), where m denotes the total number of message bits. We analyzed our RS LFSR mathematically using the feedback polynomial function for an area-sensitive design. However, the bit-wise stages of the proposed RS LFSR are simulated using the TSMC 130 nm IC design tool in the Mentor Graphics platform. Experimental results showed that the proposed LFSR achieved the effective pseudo-random test patterns with a low-test power dissipation (25.13 µW). Ultimately, the circuit has operated in the highest operating frequency (1.9 GHz) environment.   &nbsp

MorphIC: A 65-nm 738k-Synapse/mm2^2 Quad-Core Binary-Weight Digital Neuromorphic Processor with Stochastic Spike-Driven Online Learning

Recent trends in the field of neural network accelerators investigate weight quantization as a means to increase the resource- and power-efficiency of hardware devices. As full on-chip weight storage is necessary to avoid the high energy cost of off-chip memory accesses, memory reduction requirements for weight storage pushed toward the use of binary weights, which were demonstrated to have a limited accuracy reduction on many applications when quantization-aware training techniques are used. In parallel, spiking neural network (SNN) architectures are explored to further reduce power when processing sparse event-based data streams, while on-chip spike-based online learning appears as a key feature for applications constrained in power and resources during the training phase. However, designing power- and area-efficient spiking neural networks still requires the development of specific techniques in order to leverage on-chip online learning on binary weights without compromising the synapse density. In this work, we demonstrate MorphIC, a quad-core binary-weight digital neuromorphic processor embedding a stochastic version of the spike-driven synaptic plasticity (S-SDSP) learning rule and a hierarchical routing fabric for large-scale chip interconnection. The MorphIC SNN processor embeds a total of 2k leaky integrate-and-fire (LIF) neurons and more than two million plastic synapses for an active silicon area of 2.86mm$^2$ in 65nm CMOS, achieving a high density of 738k synapses/mm$^2$. MorphIC demonstrates an order-of-magnitude improvement in the area-accuracy tradeoff on the MNIST classification task compared to previously-proposed SNNs, while having no penalty in the energy-accuracy tradeoff.Comment: This document is the paper as accepted for publication in the IEEE Transactions on Biomedical Circuits and Systems journal (2019), the fully-edited paper is available at https://ieeexplore.ieee.org/document/876400

A low-energy rate-adaptive bit-interleaved passive optical network

Energy consumption of customer premises equipment (CPE) has become a serious issue in the new generations of time-division multiplexing passive optical networks, which operate at 10 Gb/s or higher. It is becoming a major factor in global network energy consumption, and it poses problems during emergencies when CPE is battery-operated. In this paper, a low-energy passive optical network (PON) that uses a novel bit-interleaving downstream protocol is proposed. The details about the network architecture, protocol, and the key enabling implementation aspects, including dynamic traffic interleaving, rate-adaptive descrambling of decimated traffic, and the design and implementation of a downsampling clock and data recovery circuit, are described. The proposed concept is shown to reduce the energy consumption for protocol processing by a factor of 30. A detailed analysis of the energy consumption in the CPE shows that the interleaving protocol reduces the total energy consumption of the CPE significantly in comparison to the standard 10 Gb/s PON CPE. Experimental results obtained from measurements on the implemented CPE prototype confirm that the CPE consumes significantly less energy than the standard 10 Gb/s PON CPE

Analysis and design implementation using VHDL and NAND circuit for Linear Feedback Shift Register (LFSR) for Low Power BIST

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1D Cellular Automata for Pulse Width Modulated Compressive Sampling CMOS Image Sensors

Compressive sensing (CS) is an alternative to the Shannon limit when the signal to be acquired is known to be sparse or compressible in some domain. Since compressed samples are non-hierarchical packages of information, this acquisition technique can be employed to overcome channel losses and restricted data rates. The quality of the compressed samples that a sensor can deliver is affected by the measurement matrix used to collect them. Measurement matrices usually employed in CS image sensors are recursive random-like binary matrices obtained using pseudo-random number generators (PRNG). In this paper we analyse the performance of these PRNGs in order to understand how their non-idealities affect the quality of the compressed samples. We present the architecture of a CMOS image sensor that uses class-III elementary cellular automata (ECA) and pixel pulse width modulation (PWM) to generate onchip a measurement matrix and high the quality compressed samples.Ministerio de Economía y Competitividad TEC2015-66878-C3-1-RJunta de Andalucía TIC 2338-2013Office of Naval Research N000141410355CONACYT (Mexico) MZO-2017-29106

Power Droop Reduction In Logic BIST By Scan Chain Reordering

Significant peak power (PP), thus power droop (PD), during test is a serious concern for modern, complex ICs. In fact, the PD originated during the application of test vectors may produce a delay effect on the circuit under test signal transitions. This event may be erroneously recognized as presence of a delay fault, with consequent generation of an erroneous test fail, thus increasing yield loss. Several solutions have been proposed in the literature to reduce the PD during test of combinational ICs, while fewer approaches exist for sequential ICs. In this paper, we propose a novel approach to reduce peak power/power droop during test of sequential circuits with scan-based Logic BIST. In particular, our approach reduces the switching activity of the scan chains between following capture cycles. This is achieved by an original generation and arrangement of test vectors. The proposed approach presents a very low impact on fault coverage and test time