Intelligent video surveillance: a review through deep learning techniques for crowd analysis
Big data applications are consuming most of the space in industry and research area.
Among the widespread examples of big data, the role of video streams from CCTV cameras …
Among the widespread examples of big data, the role of video streams from CCTV cameras …
Spiking neural networks for inference and learning: A memristor-based design perspective
On metrics of density and power efficiency, neuromorphic technologies have the potential to
surpass mainstream computing technologies in tasks where real-time functionality …
surpass mainstream computing technologies in tasks where real-time functionality …
Implementation of convolutional neural networks in memristor crossbar arrays with binary activation and weight quantization
We propose a hardware-friendly architecture of a convolutional neural network using a 32×
32 memristor crossbar array having an overshoot suppression layer. The gradual switching …
32 memristor crossbar array having an overshoot suppression layer. The gradual switching …
Long short term memory based hardware prefetcher: a case study
Hardware prefetching is an efficient mechanism to hide cache miss penalties. Accuracy,
coverage, and timeliness are three primary metrics in evaluating prefetcher performance …
coverage, and timeliness are three primary metrics in evaluating prefetcher performance …
A soft-pruning method applied during training of spiking neural networks for in-memory computing applications
Inspired from the computational efficiency of the biological brain, spiking neural networks
(SNNs) emulate biological neural networks, neural codes, dynamics, and circuitry. SNNs …
(SNNs) emulate biological neural networks, neural codes, dynamics, and circuitry. SNNs …
IR-QNN framework: An IR drop-aware offline training of quantized crossbar arrays
Resistive Crossbar Arrays present an elegant implementation solution for Deep Neural
Networks acceleration. The Matrix-Vector Multiplication, which is the corner-stone of DNNs …
Networks acceleration. The Matrix-Vector Multiplication, which is the corner-stone of DNNs …
PCM-based analog compute-in-memory: Impact of device non-idealities on inference accuracy
The impact of phase change memory (PCM) device non-idealities on the deep neural
network (DNN) inference accuracy is systematically investigated. Based on the experimental …
network (DNN) inference accuracy is systematically investigated. Based on the experimental …
Cross-point resistive memory: Nonideal properties and solutions
Emerging computational resistive memory is promising to overcome the challenges of
scalability and energy efficiency that DRAM faces and also break through the memory wall …
scalability and energy efficiency that DRAM faces and also break through the memory wall …
Input voltage mapping optimized for resistive memory-based deep neural network hardware
Artificial neural network (ANN) computations based on graphics processing units (GPUs)
consume high power. Resistive random-access memory (RRAM) has been gaining attention …
consume high power. Resistive random-access memory (RRAM) has been gaining attention …
Resistive neural hardware accelerators
K Smagulova, ME Fouda, F Kurdahi… - Proceedings of the …, 2023 - ieeexplore.ieee.org
Deep neural networks (DNNs), as a subset of machine learning (ML) techniques, entail that
real-world data can be learned, and decisions can be made in real time. However, their wide …
real-world data can be learned, and decisions can be made in real time. However, their wide …