55 citations · 340 across the 35 of their papers we have counts for
38 papers
MDTransformer: A Hardware-Software Co-Design of Mode-Division Photonic Transformer Accelerator with Inverse-Designed Coherent Crossbar
Solomon Micheal Serunjogi, Rachmad Vidya Wicaksana Putra, Ayat Taha +2
Recently, photonic transformer accelerators (PTAs) have successfully achieved significant speedup and energy efficiency improvements over electronic accelerators for expediting Tra…
AQ4SViT: An Automated Quantization Framework with Search Gating Policy for Compressing Spiking Vision Transformers
Rachmad Vidya Wicaksana Putra, Saad Iftikhar, Muhammad Shafique
Spiking Vision Transformers (SViTs) have emerged as alternative low-power ViT models, but their large sizes hinder their deployments on resource-constrained embedded AI systems. To…
QuBLAST: A Framework for Quantizing Large Language Models with Block-Level Compression Approach and Activation Scaling Strategy
Pasindu Wickramasinghe, Achyuta Muthuvelan, Rachmad Vidya Wicaksana Putra +2
LLMs have become the state-of-the-art algorithms for solving NLP tasks. However, they typically come at huge computational and memory costs, thus making them difficult to deploy on…
DxPTA: An Architecture Design Space Exploration with Optical Dataflow-guided Strategy for HW/SW Co-Design of Photonic Transformer Accelerators
Rachmad Vidya Wicaksana Putra, Solomon Micheal Serunjogi, Mahmoud Rasras +1
Transformer-based networks have emerged as prominent AI models with state-of-the-art performance, which potentially pave the way toward artificial general intelligence (AGI). Howev…
PrimeSVT: An Automated Memory-aware Pruning Framework with Prioritized Compression Policy for Spiking Vision Transformers
Rachmad Vidya Wicaksana Putra, Achyuta Muthuvelan, Alberto Marchisio +1
The large sizes of Spiking Vision Transformers (SViTs) still hinder their embedded implementation, highlighting the need for model compression. State-of-the-art works compress SViT…
PSViT: A Methodology for Structurally Pruning Spiking Vision Transformers
Rachmad Vidya Wicaksana Putra, Achyuta Muthuvelan, Alberto Marchisio +1
Spiking Vision Transformer (SViT) models are promising low-power ViT models for solving vision-based tasks with state-of-the-art performance. However, their large sizes limit their…