45 citations · 166 across the 41 of their papers we have counts for
42 papers
Node-wise Feature Encoding for Neural Performance Prediction
Matthew Grenier, William Hammer, Andrew Heuer +3
As neural networks are increasingly deployed on resource constrained edge devices, accurate prediction of latency and energy is critical for efficient neural architecture search. E…
ARMOR-IMC: Adaptive Resource Mapping for Operational Robustness via Secure In-Memory Computing
Muhtasim Alam Chowdhury, Ramtin Zand, Soheil Salehi
The massive data-movement overhead in traditional architectures has led to the adoption of In-Memory Computing (IMC) for energy-efficient Deep Neural Network (DNN) processing. By l…
Rep Smarter, Not Harder: AI Hypertrophy Coaching with Wearable Sensors and Edge Neural Networks
Grant King, Musa Azeem, Savannah Noblitt +2
Optimizing resistance training for hypertrophy requires balancing proximity to muscular failure, often quantified by Repetitions in Reserve (RiR), with fatigue management. However,…
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…
CrossNAS: A Cross-Layer Neural Architecture Search Framework for PIM Systems
Md Hasibul Amin, Mohammadreza Mohammadi, Jason D. Bakos +1
In this paper, we propose the CrossNAS framework, an automated approach for exploring a vast, multidimensional search space that spans various design abstraction layers-circuits, a…
NSF-MAP: Neurosymbolic Multimodal Fusion for Robust and Interpretable Anomaly Prediction in Assembly Pipelines
Chathurangi Shyalika, Renjith Prasad, Fadi El Kalach +4
In modern assembly pipelines, identifying anomalies is crucial in ensuring product quality and operational efficiency. Conventional single-modality methods fail to capture the intr…
LLMPi: Optimizing LLMs for High-Throughput on Raspberry Pi
Mahsa Ardakani, Jinendra Malekar, Ramtin Zand
Deploying Large Language Models (LLMs) on resource-constrained edge devices like the Raspberry Pi presents challenges in computational efficiency, power consumption, and response l…
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…
XbarSim: A Decomposition-Based Memristive Crossbar Simulator
Anzhelika Kolinko, Md Hasibul Amin, Ramtin Zand +1
Given the growing focus on memristive crossbar-based in-memory computing (IMC) architectures as a potential alternative to current energy-hungry machine learning hardware, the avai…
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…
AssemAI: Interpretable Image-Based Anomaly Detection for Manufacturing Pipelines
Renjith Prasad, Chathurangi Shyalika, Ramtin Zand +4
Anomaly detection in manufacturing pipelines remains a critical challenge, intensified by the complexity and variability of industrial environments. This paper introduces AssemAI,…
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…
Realtime Facial Expression Recognition: Neuromorphic Hardware vs. Edge AI Accelerators
Heath Smith, James Seekings, Mohammadreza Mohammadi +1
The paper focuses on real-time facial expression recognition (FER) systems as an important component in various real-world applications such as social robotics. We investigate two…
Facial Expression Recognition at the Edge: CPU vs GPU vs VPU vs TPU
Mohammadreza Mohammadi, Heath Smith, Lareb Khan +1
Facial Expression Recognition (FER) plays an important role in human-computer interactions and is used in a wide range of applications. Convolutional Neural Networks (CNN) have sho…
A Novel Stochastic LSTM Model Inspired by Quantum Machine Learning
Joseph Lindsay, Ramtin Zand
Works in quantum machine learning (QML) over the past few years indicate that QML algorithms can function just as well as their classical counterparts, and even outperform them in…
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…
IMAC-Sim: A Circuit-level Simulator For In-Memory Analog Computing Architectures
Md Hasibul Amin, Mohammed E. 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 machine learning applicati…