45 citations · 166 across the 41 of their papers we have counts for
11 papers · 1 filter
FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design
Mahmoud Nazzal, Khoa Nguyen, Deepak Vungarala +4
AI hardware design is advancing rapidly, driven by the promise of design automation to make chip development faster, more efficient, and more accessible to a wide range of users. A…
PIM-LLM: A High-Throughput Hybrid PIM Architecture for 1-bit LLMs
Jinendra Malekar, Peyton Chandarana, Md Hasibul Amin +2
In this paper, we propose PIM-LLM, a hybrid architecture developed to accelerate 1-bit large language models (LLMs). PIM-LLM leverages analog processing-in-memory (PIM) architectur…
TPU-Gen: LLM-Driven Custom Tensor Processing Unit Generator
Deepak Vungarala, Mohammed E. Elbtity, Sumiya Syed +5
The increasing complexity and scale of Deep Neural Networks (DNNs) necessitate specialized tensor accelerators, such as Tensor Processing Units (TPUs), to meet various computationa…
LIMCA: LLM for Automating Analog In-Memory Computing Architecture Design Exploration
Deepak Vungarala, Md Hasibul Amin, Pietro Mercati +5
Resistive crossbars enabling analog In-Memory Computing (IMC) have emerged as a promising architecture for Deep Neural Network (DNN) acceleration, offering high memory bandwidth an…
Learnable Sparsification of Die-to-Die Communication via Spike-Based Encoding
Joshua Nardone, Ruijie Zhu, Joseph Callenes +3
Efficient communication is central to both biological and artificial intelligence (AI) systems. In biological brains, the challenge of long-range communication across regions is ad…
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…
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…
Heterogeneous Integration of In-Memory Analog Computing Architectures with Tensor Processing Units
Mohammed E. Elbtity, Brendan Reidy, Md Hasibul Amin +1
Tensor processing units (TPUs), specialized hardware accelerators for machine learning tasks, have shown significant performance improvements when executing convolutional layers in…
A Python Framework for SPICE Circuit Simulation of In-Memory Analog Computing Circuits
Md Hasibul Amin, Mohammed Elbtity, Ramtin Zand
With the increased attention to memristive-based in-memory analog computing (IMAC) architectures as an alternative for energy-hungry computer systems for data-intensive application…
Interconnect Parasitics and Partitioning in Fully-Analog In-Memory Computing Architectures
Md Hasibul Amin, Mohammed Elbtity, Ramtin Zand
Fully-analog in-memory computing (IMC) architectures that implement both matrix-vector multiplication and non-linear vector operations within the same memory array have shown promi…
An In-Memory Analog Computing Co-Processor for Energy-Efficient CNN Inference on Mobile Devices
Mohammed Elbtity, Abhishek Singh, Brendan Reidy +2
In this paper, we develop an in-memory analog computing (IMAC) architecture realizing both synaptic behavior and activation functions within non-volatile memory arrays. Spin-orbit…