13 citations · 24 across the 13 of their papers we have counts for
6 papers · 1 filter
BenDi: An Energy-Efficient Quasi-Stochastic Systolic Architecture for Edge Bioelectronics
Bochen Ye, Yihan Pan, Shady Agwa +1
Continuous long-term monitoring and diagnosis of biomedical signals, such as electrocardiograms (ECGs), can help mitigate an increasing threat to public health. Artificial Intellig…
DISCA: A Digital In-memory Stochastic Computing Architecture Using A Compressed Bent-Pyramid Format
Shady Agwa, Yikang Shen, Shiwei Wang +1
Nowadays, we are witnessing an Artificial Intelligence revolution that dominates the technology landscape in various application domains, such as healthcare, robotics, automotive,…
OISMA: On-the-fly In-memory Stochastic Multiplication Architecture for Matrix-Multiplication Workloads
Shady Agwa, Yihan Pan, Georgios Papandroulidakis +1
Artificial intelligence (AI) models are currently driven by a significant upscaling of their complexity, with massive matrix-multiplication workloads representing the major computa…
3D-TrIM: A Memory-Efficient Spatial Computing Architecture for Convolution Workloads
Cristian Sestito, Ahmed J. Abdelmaksoud, Shady Agwa +1
The Von Neumann bottleneck, which relates to the energy cost of moving data from memory to on-chip core and vice versa, is a serious challenge in state-of-the-art AI architectures,…
DiP: A Scalable, Energy-Efficient Systolic Array for Matrix Multiplication Acceleration
Ahmed J. Abdelmaksoud, Shady Agwa, Themis Prodromakis
Transformers are gaining increasing attention across Natural Language Processing (NLP) application domains due to their outstanding accuracy. However, these data-intensive models a…
TrIM, Triangular Input Movement Systolic Array for Convolutional Neural Networks: Architecture and Hardware Implementation
Cristian Sestito, Shady Agwa, Themis Prodromakis
Modern hardware architectures for Convolutional Neural Networks (CNNs), other than targeting high performance, aim at dissipating limited energy. Reducing the data movement cost be…