A survey of stochastic computing neural networks for machine learning applications
Neural networks (NNs) are effective machine learning models that require significant
hardware and energy consumption in their computing process. To implement NNs …
hardware and energy consumption in their computing process. To implement NNs …
Stochastic computing in convolutional neural network implementation: A review
YY Lee, ZA Halim - PeerJ Computer Science, 2020 - peerj.com
Stochastic computing (SC) is an alternative computing domain for ubiquitous deterministic
computing whereby a single logic gate can perform the arithmetic operation by exploiting the …
computing whereby a single logic gate can perform the arithmetic operation by exploiting the …
Chaotic neural network quantization and its robustness against adversarial attacks
Achieving robustness against adversarial attacks while maintaining high accuracy remains a
critical challenge in neural networks. Parameter quantization is one of the main approaches …
critical challenge in neural networks. Parameter quantization is one of the main approaches …
Stochastic computing convolutional neural network architecture reinvented for highly efficient artificial intelligence workload on field-programmable gate array
Stochastic computing (SC) has a substantial amount of study on application-specific
integrated circuit (ASIC) design for artificial intelligence (AI) edge computing, especially the …
integrated circuit (ASIC) design for artificial intelligence (AI) edge computing, especially the …
Deploying deep learning networks based advanced techniques for image processing on FPGA platform
R Ghodhbani, T Saidani, H Zayeni - Neural Computing and Applications, 2023 - Springer
Convolutional neural networks (CNN) have emerged as a dominant deep learning
technique in various fields, including image processing, computer vision, and intelligent …
technique in various fields, including image processing, computer vision, and intelligent …
Low-cost stochastic hybrid multiplier for quantized neural networks
With increased interests of neural networks, hardware implementations of neural networks
have been investigated. Researchers pursue low hardware cost by using different …
have been investigated. Researchers pursue low hardware cost by using different …
Hardware-software co-optimization of long-latency stochastic computing
Stochastic computing (SC) is an emerging paradigm that offers hardware-efficient solutions
for develo** low-cost and noise-robust architectures. In SC, deterministic logic systems …
for develo** low-cost and noise-robust architectures. In SC, deterministic logic systems …
E2BNet: MAC-free yet accurate 2-level binarized neural network accelerator for embedded systems
Deep neural networks are widely used in computer vision, pattern recognition, and speech
recognition and achieve high accuracy at the cost of remarkable computation. High …
recognition and achieve high accuracy at the cost of remarkable computation. High …
Implementation of tiny machine learning models on arduino 33 ble for gesture and speech recognition
R Prasanna, PC Kakarla, VS PJ, N Mohan - arxiv preprint arxiv …, 2022 - arxiv.org
In this article gesture recognition and speech recognition applications are implemented on
embedded systems with Tiny Machine Learning (TinyML). It features 3-axis accelerometer, 3 …
embedded systems with Tiny Machine Learning (TinyML). It features 3-axis accelerometer, 3 …
Neural network classifiers using a hardware-based approximate activation function with a hybrid stochastic multiplier
Neural networks are becoming prevalent in many areas, such as pattern recognition and
medical diagnosis. Stochastic computing is one potential solution for neural networks …
medical diagnosis. Stochastic computing is one potential solution for neural networks …