Energy efficient parallel neuromorphic architectures with approximate arithmetic on FPGA

Design of Energy - Efficient Approximate Arithmetic Circuits

Energy-Efficient Neuromorphic Classifiers

Neuromorphic engineering combines the architectural and computational principles of systems neuroscience with semiconductor electronics, with the aim of building efficient and compact devices that mimic the synaptic and neural machinery of the brain. The energy consumptions promised by neuromorphic engineering are extremely low, comparable to those of the nervous system. Until now, however, the...

Energy-Efficient FPGA-Based Parallel Quasi-Stochastic Computing

The high performance of FPGA (Field Programmable Gate Array) in image processing applications is justified by its flexible reconfigurability, its inherent parallel nature and the availability of a large amount of internal memories. Lately, the Stochastic Computing (SC) paradigm has been found to be significantly advantageous in certain application domains including image processing because of i...

Exploiting silicon photonics for energy-efficient heterogeneous parallel architectures

Welcome to this special issue of the journal Concurrency and Computation: Practice and Experience on Exploiting Silicon Photonics for Energy-Efficient Heterogeneous Parallel Architectures, which contains five original manuscripts that cover a complete range of perspectives. Silicon photonics is undoubtedly expected to play a big role in the evolution in board, crosschip, interposer-level and on...

Backpropagation for Energy-Efficient Neuromorphic Computing

Solving real world problems with embedded neural networks requires both training algorithms that achieve high performance and compatible hardware that runs in real time while remaining energy efficient. For the former, deep learning using backpropagation has recently achieved a string of successes across many domains and datasets. For the latter, neuromorphic chips that run spiking neural netwo...