1 citations · 2 across the 3 of their papers we have counts for
8 papers
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,…
A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference
Kieran Woodward, Eiman Kanjo, Georgios Papandroulidakis +2
In recent years, the development of smart edge computing systems to process information locally is on the rise. Many near-sensor machine learning (ML) approaches have been implemen…
L-Sort: On-chip Spike Sorting with Efficient Median-of-Median Detection and Localization-based Clustering
Yuntao Han, Yihan Pan, Xiongfei Jiang +4
Spike sorting is a critical process for decoding large-scale neural activity from extracellular recordings. The advancement of neural probes facilitates the recording of a high num…
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…