23 citations · 50 across the 12 of their papers we have counts for
12 papers
Matmul or No Matmul in the Era of 1-bit LLMs
Jinendra Malekar, Mohammed E. Elbtity, Ramtin Zand
The advent of 1-bit large language models (LLMs) has attracted considerable attention and opened up new research opportunities. However, 1-bit LLMs only improve a fraction of model…
HiRISE: High-Resolution Image Scaling for Edge ML via In-Sensor Compression and Selective ROI
Brendan Reidy, Sepehr Tabrizchi, Mohamadreza Mohammadi +3
With the rise of tiny IoT devices powered by machine learning (ML), many researchers have directed their focus toward compressing models to fit on tiny edge devices. Recent works h…
Towards Efficient Deployment of Hybrid SNNs on Neuromorphic and Edge AI Hardware
James Seekings, Peyton Chandarana, Mahsa Ardakani +2
This paper explores the synergistic potential of neuromorphic and edge computing to create a versatile machine learning (ML) system tailored for processing data captured by dynamic…
Flex-TPU: A Flexible TPU with Runtime Reconfigurable Dataflow Architecture
Mohammed Elbtity, Peyton Chandarana, Ramtin Zand
Tensor processing units (TPUs) are one of the most well-known machine learning (ML) accelerators utilized at large scale in data centers as well as in tiny ML applications. TPUs of…
Multi-Objective Neural Architecture Search for In-Memory Computing
Md Hasibul Amin, Mohammadreza Mohammadi, Ramtin Zand
In this work, we employ neural architecture search (NAS) to enhance the efficiency of deploying diverse machine learning (ML) tasks on in-memory computing (IMC) architectures. Init…
Lightator: An Optical Near-Sensor Accelerator with Compressive Acquisition Enabling Versatile Image Processing
Mehrdad Morsali, Brendan Reidy, Deniz Najafi +6
This paper proposes a high-performance and energy-efficient optical near-sensor accelerator for vision applications, called Lightator. Harnessing the promising efficiency offered b…