PNeuro: A scalable energy-efficient programmable hardware accelerator for neural networks

2018 ◽  
Author(s):  
A. Carbon ◽  
J.-M. Philippe ◽  
O. Bichler ◽  
R. Schmit ◽  
B. Tain ◽  
...  
Keyword(s):  
Neural Networks ◽  
Energy Efficient ◽  
Hardware Accelerator ◽  
Programmable Hardware

Energy efficient biomorphic pulse information coding in electronic neurons for the entrance unit of the neuroprocessor

2019 ◽  
Vol 5 (3) ◽  
pp. 186-212 ◽  
Author(s):  
Alexander D. Pisarev
Keyword(s):  
Neural Networks ◽  
Energy Efficiency ◽  
Energy Efficient ◽  
Information Transfer ◽  
Biological Systems ◽  
Technological Parameters ◽  
Input Unit ◽  
Efficient Operation ◽  
Electric Circuits ◽  
Electronic Neuron

This article studies the implementation of some well-known principles of information work of biological systems in the input unit of the neuroprocessor, including spike coding of information used in models of neural networks of the latest generation.<br> The development of modern neural network IT gives rise to a number of urgent tasks at the junction of several scientific disciplines. One of them is to create a hardware platform&nbsp;— a neuroprocessor for energy-efficient operation of neural networks. Recently, the development of nanotechnology of the main units of the neuroprocessor relies on combined memristor super-large logical and storage matrices. The matrix topology is built on the principle of maximum integration of programmable links between nodes. This article describes a method for implementing biomorphic neural functionality based on programmable links of a highly integrated 3D logic matrix.<br> This paper focuses on the problem of achieving energy efficiency of the hardware used to model neural networks. The main part analyzes the known facts of the principles of information transfer and processing in biological systems from the point of view of their implementation in the input unit of the neuroprocessor. The author deals with the scheme of an electronic neuron implemented based on elements of a 3D logical matrix. A pulsed method of encoding input information is presented, which most realistically reflects the principle of operation of a sensory biological neural system. The model of an electronic neuron for selecting ranges of technological parameters in a real 3D logic matrix scheme is analyzed. The implementation of disjunctively normal forms is shown, using the logic function in the input unit of a neuroprocessor as an example. The results of modeling fragments of electric circuits with memristors of a 3D logical matrix in programming mode are presented.<br> The author concludes that biomorphic pulse coding of standard digital signals allows achieving a high degree of energy efficiency of the logic elements of the neuroprocessor by reducing the number of valve operations. Energy efficiency makes it possible to overcome the thermal limitation of the scalable technology of three-dimensional layout of elements in memristor crossbars.

scholarly journals A Programmable Hardware Accelerator for Simulating Dynamical Systems

2017 ◽  
Vol 45 (2) ◽  
pp. 403-415 ◽  
Author(s):  
Jaeha Kung ◽  
Yun Long ◽  
Duckhwan Kim ◽  
Saibal Mukhopadhyay
Keyword(s):  
Dynamical Systems ◽  
Hardware Accelerator ◽  
Programmable Hardware

scholarly journals NanoLEDs for energy-efficient and gigahertz-speed spike-based sub-λ neuromorphic nanophotonic computing

Nanophotonics ◽  
2020 ◽  
Vol 9 (13) ◽  
pp. 4149-4162 ◽  
Author(s):  
Bruno Romeira ◽  
José M. L. Figueiredo ◽  
Julien Javaloyes
Keyword(s):  
Neural Networks ◽  
Energy Efficient ◽  
High Speed ◽  
Dynamic Properties ◽  
Optical Computing ◽  
Differential Conductance ◽  
Rate Equations ◽  
Dynamical Equations ◽  
Electrical Modulation ◽  
High Bandwidth

AbstractEvent-activated biological-inspired subwavelength (sub-λ) photonic neural networks are of key importance for future energy-efficient and high-bandwidth artificial intelligence systems. However, a miniaturized light-emitting nanosource for spike-based operation of interest for neuromorphic optical computing is still lacking. In this work, we propose and theoretically analyze a novel nanoscale nanophotonic neuron circuit. It is formed by a quantum resonant tunneling (QRT) nanostructure monolithic integrated into a sub-λ metal-cavity nanolight-emitting diode (nanoLED). The resulting optical nanosource displays a negative differential conductance which controls the all-or-nothing optical spiking response of the nanoLED. Here we demonstrate efficient activation of the spiking response via high-speed nonlinear electrical modulation of the nanoLED. A model that combines the dynamical equations of the circuit which considers the nonlinear voltage-controlled current characteristic, and rate equations that takes into account the Purcell enhancement of the spontaneous emission, is used to provide a theoretical framework to investigate the optical spiking dynamic properties of the neuromorphic nanoLED. We show inhibitory- and excitatory-like optical spikes at multi-gigahertz speeds can be achieved upon receiving exceptionally low (sub-10 mV) synaptic-like electrical activation signals, lower than biological voltages of 100 mV, and with remarkably low energy consumption, in the range of 10–100 fJ per emitted spike. Importantly, the energy per spike is roughly constant and almost independent of the incoming modulating frequency signal, which is markedly different from conventional current modulation schemes. This method of spike generation in neuromorphic nanoLED devices paves the way for sub-λ incoherent neural elements for fast and efficient asynchronous neural computation in photonic spiking neural networks.

Energy-efficient charge sharing-based 8T2C SRAM in-memory accelerator for binary neural networks in 28nm CMOS

2021 ◽  
Author(s):  
Hyunmyung Oh ◽  
Hyungjun Kim ◽  
Daehyun Ahn ◽  
Jihoon Park ◽  
Yulhwa Kim ◽  
...  
Keyword(s):  
Neural Networks ◽  
Energy Efficient ◽  
Charge Sharing ◽  
Efficient Charge

Compensated-DNN: Energy Efficient Low-Precision Deep Neural Networks by Compensating Quantization Errors

2018 ◽  
Author(s):  
Shubham Jain ◽  
Swagath Venkataramani ◽  
Vijayalakshmi Srinivasan ◽  
Jungwook Choi ◽  
Pierce Chuang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Energy Efficient ◽  
Deep Neural Networks
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