By A Mystery Man Writer
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.
Free Residential Electronic Recycling Event – Town of Star Prairie
Solvent-free adhesive ionic elastomer for multifunctional
Electronics, Free Full-Text
/electronics/electronics-09-00483/arti
Bitcoin Logo in Mining Rig Computer Chip Free Stock Photo
All Brand Electronics Vector Logo - Download Free SVG Icon
Electronics, Free Full-Text, mod player action optimization
Zigbee Complete IOT Solution - CSA-IOT, zigbee
Electronics, Free Full-Text
Electronics, Free Full-Text
TCL Launches World's First Smartphones Featuring NXTPAPER Technology
Valdosta, Lowndes community electronics recycling event - Valdosta
Electronics, Free Full-Text, dc-dc boost converter
Data Centric Publish Subscribe Flash Sales
Free Vector Electronic sale poster