activity
20172026
most citedEmulating homoeostatic effects with metal-oxide memristors T-dependence

2 citations · 4 across the 16 of their papers we have counts for

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8 papers · 1 filter

cs.AR2026

Gen-TAS: A Generative AI-Aided Hardware-Software Task Allocation Framework for FPGA-GPP Heterogeneous Systems

Mary Kong, Yuqin Zhao, Semih Vazgecen +2

FPGA-GPP heterogeneous systems combine software flexibility with the performance and energy efficiency of reconfigurable hardware. However, determining which application tasks shou…

cs.AR2026

LLMs in Digital EDA: A perspective on shifting roles from Generation to Orchestration

Matthew Youngman, Cristian Sestito, Themis Prodromakis

Electronic design automation (EDA) has advanced engineering productivity through successive generations of tooling that progressively automate synthesis, optimisation, and verifica…

cs.AR2026

D-Legion: A Scalable Many-Core Architecture for Accelerating Matrix Multiplication in Quantized LLMs

Ahmed J. Abdelmaksoud, Cristian Sestito, Shiwei Wang +1

The performance gains obtained by large language models (LLMs) are closely linked to their substantial computational and memory requirements. Quantized LLMs offer significant advan…

cs.AR2025

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,…

cs.AR2025

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…

cs.AR2025

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,…