6 citations · 8 across the 5 of their papers we have counts for
6 papers
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…
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…
WellDunn: On the Robustness and Explainability of Language Models and Large Language Models in Identifying Wellness Dimensions
Seyedali Mohammadi, Edward Raff, Jinendra Malekar +3
Language Models (LMs) are being proposed for mental health applications where the heightened risk of adverse outcomes means predictive performance may not be a sufficient litmus te…
Process Knowledge-infused Learning for Clinician-friendly Explanations
Kaushik Roy, Yuxin Zi, Manas Gaur +4
Language models have the potential to assess mental health using social media data. By analyzing online posts and conversations, these models can detect patterns indicating mental…
Demo Alleviate: Demonstrating Artificial Intelligence Enabled Virtual Assistance for Telehealth: The Mental Health Case
Kaushik Roy, Vedant Khandelwal, Raxit Goswami +3
After the pandemic, artificial intelligence (AI) powered support for mental health care has become increasingly important. The breadth and complexity of significant challenges requ…