Neurobench: Advancing neuromorphic computing through collaborative, fair and representative benchmarking
J Yik, SH Ahmed, Z Ahmed, B Anderson, AG Andreou… - ar**
We propose a fully integrated low-power keyword spotting (KWS) system on chip (SoC) with
content-adaptive frame subsampling, implemented in 28-nm CMOS technology. The system …
content-adaptive frame subsampling, implemented in 28-nm CMOS technology. The system …
[PDF][PDF] Comprehensive review of optimal utilization of clock and power resources in multi bit flip flop techniques
Abstract Objective: To analyze High-speed digital Integrated Circuit (IC) designing
techniques and to identify the power dissipation rate in a different configuration of network …
techniques and to identify the power dissipation rate in a different configuration of network …
MorphBungee: An edge neuromorphic chip for high-accuracy on-chip learning of multiple-layer spiking neural networks
T Wang, H Wang, J He, Z Zhong, F Tang… - … Circuits and Systems …, 2022 - ieeexplore.ieee.org
Spiking neural networks and neuromorphic systems have attracted ever increasing interests
recently, due to their high computational efficiency by imitating the functional mechanism of …
recently, due to their high computational efficiency by imitating the functional mechanism of …
Versatile cmos analog lif neuron for memristor-integrated neuromorphic circuits
Heterogeneous systems with analog CMOS circuits integrated with nanoscale memristive
devices enable efficient deployment of neural networks on neuromorphic hardware. CMOS …
devices enable efficient deployment of neural networks on neuromorphic hardware. CMOS …
Time-series forecasting of microbial fuel cell energy generation using deep learning
A Hess-Dunlop, H Kakani, S Taylor, D Louie… - Frontiers in Computer …, 2025 - frontiersin.org
Soil microbial fuel cells (SMFCs) are an emerging technology which offer clean and
renewable energy in environments where more traditional power sources, such as chemical …
renewable energy in environments where more traditional power sources, such as chemical …
MorphBungee: A 65-nm 7.2-mm2 27-μJ/image Digital Edge Neuromorphic Chip with On-Chip 802-frame/s Multi-Layer Spiking Neural Network Learning
T Wang, M Tian, H Wang, Z Zhong, J He… - … Circuits and Systems, 2024 - ieeexplore.ieee.org
This paper presents a digital edge neuromorphic spiking neural network (SNN) processor
chip for a variety of edge intelligent cognitive applications. This processor allows high …
chip for a variety of edge intelligent cognitive applications. This processor allows high …
Background Noise and Process-Variation-Tolerant Sub-Microwatt Keyword Spotting Hardware Featuring Spike-Domain Division-Based Energy Normalization
D Wang, SJ Kim, M Yang, AA Lazar… - IEEE Journal of Solid …, 2024 - ieeexplore.ieee.org
This article presents keyword spotting (KWS) hardware that uses analog signal processing
(ASP) and division-based energy normalization (DN) to perform ultralow power KWS …
(ASP) and division-based energy normalization (DN) to perform ultralow power KWS …
Quantized Context Based LIF Neurons for Recurrent Spiking Neural Networks in 45nm
SS Bezugam, Y Wu, JB Yoo… - 2024 Neuro Inspired …, 2024 - ieeexplore.ieee.org
In this study, we propose the first hardware implementation of a context-based recurrent
spiking neural network (RSNN) emphasizing on integrating dual information streams within …
spiking neural network (RSNN) emphasizing on integrating dual information streams within …
Low-voltage implementation of neuromorphic circuits for a spike-based learning control module
Recent brain emulation engines have been configured using thousands of neurons and
billions of synapses. These components make a significant impact on the whole system in …
billions of synapses. These components make a significant impact on the whole system in …