Sherief Reda

About

Sherief Reda is a professor affiliated with the Scalable Energy-Efficient Laboratory (SCALE) at the School of Engineering, Brown University. His research focuses on energy-efficient computing, scalable system design, and related areas within engineering. As the principal investigator of the SCALE lab, he leads efforts in developing innovative solutions and tools aimed at improving energy efficiency in computing systems. His work involves collaboration with a diverse group of students and scholars, contributing to advancements in scalable and sustainable engineering practices.

Research topics

• Computer Science
• Artificial Intelligence
• Combinatorial chemistry
• Theoretical computer science
• Algorithm
• Computer architecture
• Embedded system
• Computer engineering
• Chemistry
• Database

Selected publications

• Approximate Logic Synthesis: A Survey

  Proceedings of the IEEE · 2020 · 100 citations

  Senior authorCorresponding

  Approximate computing is an emerging paradigm that, by relaxing the requirement for full accuracy, offers benefits in terms of design area and power consumption. This paradigm is particularly attractive in applications where the underlying computation has inherent resilience to small errors. Such applications are abundant in many domains, including machine learning, computer vision, and signal processing. In circuit design, a major challenge is the capability to synthesize the approximate circui…

  DOI

• Multicomponent molecular memory

  Nature Communications · 2020 · 72 citations

  Multicomponent reactions enable the synthesis of large molecular libraries from relatively few inputs. This scalability has led to the broad adoption of these reactions by the pharmaceutical industry. Here, we employ the four-component Ugi reaction to demonstrate that multicomponent reactions can provide a basis for large-scale molecular data storage. Using this combinatorial chemistry we encode more than 1.8 million bits of art historical images, including a Cubist drawing by Picasso. Digital d…

  PublisherOA PDFDOI

• MTLoRA: A Low-Rank Adaptation Approach for Efficient Multi-Task Learning

  2024-06-16 · 23 citations

  articleSenior author

  Adapting models pre-trained on large-scale datasets to a variety of downstream tasks is a common strategy in deep learning. Consequently, parameter-efficient fine-tuning methods have emerged as a promising way to adapt pretrained models to different tasks while training only a minimal number of parameters. While most of these methods are designed for single-task adaptation, parameter-efficient training in Multi-Task Learning (MTL) architectures is still unexplored. In this paper, we introduce MT…

  PublisherDOI

• MetRex: A Benchmark for Verilog Code Metric Reasoning Using LLMs

  arXiv (Cornell University) · 2024-11-05 · 3 citations

  preprintOpen accessSenior author

  Large Language Models (LLMs) have been applied to various hardware design tasks, including Verilog code generation, EDA tool scripting, and RTL bug fixing. Despite this extensive exploration, LLMs are yet to be used for the task of post-synthesis metric reasoning and estimation of HDL designs. In this paper, we assess the ability of LLMs to reason about post-synthesis metrics of Verilog designs. We introduce MetRex, a large-scale dataset comprising 25,868 Verilog HDL designs and their correspond…

  PublisherOA PDFDOI

• PoliTune: Analyzing the Impact of Data Selection and Fine-Tuning on Economic and Political Biases in Large Language Models

  arXiv (Cornell University) · 2024-04-10 · 3 citations

  preprintOpen accessSenior author

  In an era where language models are increasingly integrated into decision-making and communication, understanding the biases within Large Language Models (LLMs) becomes imperative, especially when these models are applied in the economic and political domains. This work investigates the impact of fine-tuning and data selection on economic and political biases in LLMs. In this context, we introduce PoliTune, a fine-tuning methodology to explore the systematic aspects of aligning LLMs with specifi…

  PublisherOA PDFDOI

Recent grants

• II-NEW: A Platform to Advance Research in Energy-Efficient Computing

  NSF · $190k · 2013–2017

• SHF: Small: Collaborative Research: Algorithmic Techniques for Post-Silicon Characterization Using Infrared Emissions

  NSF · $200k · 2011–2014

• EAGER: Synthetic Chemical-Based Information Processing

  NSF · $300k · 2019–2022

Frequent coauthors

• Soheil Hashemi

  Providence College

  74 shared

• Hokchhay Tann

  71 shared

• R. Iris Bahar

  Brown University

  65 shared

• Jacob K. Rosenstein

  Providence College

  60 shared

• Abdullah Nazma Nowroz

  Intel (United States)

  40 shared

• Brenda M. Rubenstein

  Providence College

  36 shared

• Eunsuk Kim

  Brown University

  36 shared

• Ryan Cochran

  University of Pennsylvania

  35 shared

Labs

• SCALE labPI

  Scalable Energy-Efficient Laboratory (SCALE) at School of Engineering, Brown University. PI Sherief Reda. Lab members, publications, software tools, alumni.

Similar researchers at Brown University

• Michael L. Littmanh-83
• Maurice P Herlihyh-72
• Roberto Tamassiah-69