Workload optimized systems consisting of large number of general and special purpose cores, and with a support for shared memory programming, are slowly becoming prevalent. One of the major impediments for effective p...
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Volume Yield Diagnostics (VYD) is crucial to diagnose critical systematic yield issues from the reports obtained by testing thousands of chips. This paper presents an efficient clustering technique for VYD that has be...
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Volume Yield Diagnostics (VYD) is crucial to diagnose critical systematic yield issues from the reports obtained by testing thousands of chips. This paper presents an efficient clustering technique for VYD that has been shown to work successfully both in the simulation environment as well as on real industrial failure data.
FPGA devices have emerged as a popular platform for the rapid prototyping of biological Spiking Neural Network (SNNs) applications, offering the key requirement of reconfigurability. However, FPGAs do not efficiently ...
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ISBN:
(纸本)9780863419317
FPGA devices have emerged as a popular platform for the rapid prototyping of biological Spiking Neural Network (SNNs) applications, offering the key requirement of reconfigurability. However, FPGAs do not efficiently realise the biologically plausible neuron and synaptic models of SNNs, and current FPGA routing structures cannot accommodate the high levels of inter-neuron connectivity inherent in complex SNNs. This paper highlights and discusses the current challenges of implementing large scale SNNs on reconfigurable FPGAs. The paper proposes a novel, large scale Field Programmable Neural Network (FPNN) architecture, incorporating low power analogue synapses and SNN neurons, interconnected using a Network on Chip architecture for SNN spike packet routing and SNN configuration. Initial results on the scalability of the proposed FPNN architecture are presented.
FPGA devices have witnessed popularity in their use for the rapid prototyping of biological Spiking Neural Network (SNNs) applications, as they offer the key requirement of reconfigurability. However, FPGAs do not eff...
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FPGA devices have witnessed popularity in their use for the rapid prototyping of biological Spiking Neural Network (SNNs) applications, as they offer the key requirement of reconfigurability. However, FPGAs do not efficiently realise the biological neuron/synaptic models. Also their routing structures cannot accommodate the high levels of neuron inter-connectivity inherent in complex SNNs. This paper highlights and discusses the current challenges of implementing large scale SNNs on reconfigurable FPGAs. The paper presents a novel Field Programmable Neural Network (FPNN) architecture incorporating low power analogue synapse and a network on chip architecture for SNN routing and configuration. Initial results are presented.
In this work, we propose a novel technique for evolving transistor netlists from truth table descriptions of arbitrary digital circuits. The proposed methods incorporate the effective use of Genetic Algorithms (GAs). ...
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Reversible logic is gaining Interest in the recent past due to its less heat dissipating characteristics. It has been proved that any Boolean function can be implemented using reversible gates. In this paper we propos...
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Reversible logic is gaining interest in the recent past due to its less heat dissipating characteristics. It has been proved that any Boolean function can be implemented using reversible gates. In this paper we propos...
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Reversible logic is gaining interest in the recent past due to its less heat dissipating characteristics. It has been proved that any Boolean function can be implemented using reversible gates. In this paper we propose a set of basic sequential elements that could be used for building large reversible sequential circuits leading to logic and garbage reduction by a factor of 2 to 6 when compared to existing reversible designs reported in the literature.
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