Electronics, Free Full-Text
By A Mystery Man Writer
Description
Modern massively-parallel Graphics Processing Units (GPUs) and Machine Learning (ML) frameworks enable neural network implementations of unprecedented performance and sophistication. However, state-of-the-art GPU hardware platforms are extremely power-hungry, while microprocessors cannot achieve the performance requirements. Biologically-inspired Spiking Neural Networks (SNN) have inherent characteristics that lead to lower power consumption. We thus present a bit-serial SNN-like hardware architecture. By using counters, comparators, and an indexing scheme, the design effectively implements the sum-of-products inherent in neurons. In addition, we experimented with various strength-reduction methods to lower neural network resource usage. The proposed Spiking Hybrid Network (SHiNe), validated on an FPGA, has been found to achieve reasonable performance with a low resource utilization, with some trade-off with respect to hardware throughput and signal representation.
Electronics Appliances - WordPress theme
TCL Launches World's First Smartphones Featuring NXTPAPER Technology
2022 Electronics Free Drop-Off Day and Paper Shredding - Leeds Alabama
Electronics, Free Full-Text
Electronics, Free Full-Text
Electronics, Free Full-Text, car & vehicle electronics
Electronics, Free Full-Text, mod player action optimization
Modern Electronics August 1988 : Free Download, Borrow, and
Fragos J. Practical Electronics and Arduino in 8 Hours 2022
SOLUTION: Basic electronics pdf free download - Studypool
The Aed Detects A Shockable Rhythm Infant Shop
from
per adult (price varies by group size)