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

• A Multi-Dimensional Audit of Politically Aligned Large Language Models

  arXiv (Cornell University) · 2026-04-27

  preprintOpen accessSenior author

  As the application of Large Language Models (LLMs) spreads across various industries, there are increasing concerns about the potential for their misuse, especially in sensitive areas such as political discourse. Deliberately aligning LLMs with specific political ideologies, through prompt engineering or fine-tuning techniques, can be advantageous in use cases such as political campaigns, but requires careful consideration due to heightened risks of performance degradation, misinformation, or increased biased behavior. In this work, we propose a multi-dimensional framework inspired by Habermas' Theory of Communicative Action to audit politically aligned language models across four dimensions: effectiveness, fairness, truthfulness, and persuasiveness using automated, quantitative metrics. Applying this to nine popular LLMs aligned via fine-tuning or role-playing revealed consistent trade-offs: while larger models tend to be more effective at role-playing political ideologies and truthful in their responses, they were also less fair, exhibiting higher levels of bias in the form of angry and toxic language towards people of different ideologies. Fine-tuned models exhibited lower bias and more effective alignment than the corresponding role-playing models, but also saw a decline in performance reasoning tasks and an increase in hallucinations. Overall, all of the models tested exhibited some deficiency in at least one of the four metrics, highlighting the need for more balanced and robust alignment strategies. Ultimately, this work aims to ensure politically-aligned LLMs generate legitimate, harmless arguments, offering a framework to evaluate the responsible political alignment of these models.

  PublisherDOI

• RobuMTL: Enhancing Multi-Task Learning Robustness Against Weather Conditions

  2026-03-06

  articleSenior author

  Robust Multi-Task Learning (MTL) is crucial for autonomous systems operating in real-world environments, where adverse weather conditions can severely degrade model performance and reliability. In this paper, we introduce RobuMTL, a novel architecture designed to adaptively address visual degradation by dynamically selecting taskspecific hierarchical Low-Rank Adaptation (LoRA) modules and a LoRA expert squad based on input perturbations in a mixture-of-experts fashion. Our framework enables adaptive specialization based on input characteristics, improving robustness across diverse real-world conditions. To validate our approach, we evaluated it on the PASCAL and NYUD-v2 datasets and compared it against single-task models, standard MTL baselines, and state-ofthe-art methods. On the PASCAL benchmark, RobuMTL delivers a +2.8% average relative improvement under single perturbations and up to +44.4% under mixed weather conditions compared to the MTL baseline. On NYUD-v2, RobuMTL achieves a +9.7% average relative improvement across tasks. The code will be available at GitHub <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>.

  PublisherDOI

• A Multi-Dimensional Audit of Politically Aligned Large Language Models

  ArXiv.org · 2026-04-27

  articleOpen accessSenior author

  As the application of Large Language Models (LLMs) spreads across various industries, there are increasing concerns about the potential for their misuse, especially in sensitive areas such as political discourse. Deliberately aligning LLMs with specific political ideologies, through prompt engineering or fine-tuning techniques, can be advantageous in use cases such as political campaigns, but requires careful consideration due to heightened risks of performance degradation, misinformation, or increased biased behavior. In this work, we propose a multi-dimensional framework inspired by Habermas' Theory of Communicative Action to audit politically aligned language models across four dimensions: effectiveness, fairness, truthfulness, and persuasiveness using automated, quantitative metrics. Applying this to nine popular LLMs aligned via fine-tuning or role-playing revealed consistent trade-offs: while larger models tend to be more effective at role-playing political ideologies and truthful in their responses, they were also less fair, exhibiting higher levels of bias in the form of angry and toxic language towards people of different ideologies. Fine-tuned models exhibited lower bias and more effective alignment than the corresponding role-playing models, but also saw a decline in performance reasoning tasks and an increase in hallucinations. Overall, all of the models tested exhibited some deficiency in at least one of the four metrics, highlighting the need for more balanced and robust alignment strategies. Ultimately, this work aims to ensure politically-aligned LLMs generate legitimate, harmless arguments, offering a framework to evaluate the responsible political alignment of these models.

  PublisherOA PDF

• RobuMTL: Enhancing Multi-Task Learning Robustness Against Weather Conditions

  ArXiv.org · 2026-01-16

  articleOpen accessSenior author

  Robust Multi-Task Learning (MTL) is crucial for autonomous systems operating in real-world environments, where adverse weather conditions can severely degrade model performance and reliability. In this paper, we introduce RobuMTL, a novel architecture designed to adaptively address visual degradation by dynamically selecting task-specific hierarchical Low-Rank Adaptation (LoRA) modules and a LoRA expert squad based on input perturbations in a mixture-of-experts fashion. Our framework enables adaptive specialization based on input characteristics, improving robustness across diverse real-world conditions. To validate our approach, we evaluated it on the PASCAL and NYUD-v2 datasets and compared it against single-task models, standard MTL baselines, and state-of-the-art methods. On the PASCAL benchmark, RobuMTL delivers a +2.8% average relative improvement under single perturbations and up to +44.4% under mixed weather conditions compared to the MTL baseline. On NYUD-v2, RobuMTL achieves a +9.7% average relative improvement across tasks. The code is available at GitHub.

  PublisherOA PDF

• RobuMTL: Enhancing Multi-Task Learning Robustness Against Weather Conditions

  arXiv (Cornell University) · 2026-01-16

  preprintOpen accessSenior author

  Robust Multi-Task Learning (MTL) is crucial for autonomous systems operating in real-world environments, where adverse weather conditions can severely degrade model performance and reliability. In this paper, we introduce RobuMTL, a novel architecture designed to adaptively address visual degradation by dynamically selecting task-specific hierarchical Low-Rank Adaptation (LoRA) modules and a LoRA expert squad based on input perturbations in a mixture-of-experts fashion. Our framework enables adaptive specialization based on input characteristics, improving robustness across diverse real-world conditions. To validate our approach, we evaluated it on the PASCAL and NYUD-v2 datasets and compared it against single-task models, standard MTL baselines, and state-of-the-art methods. On the PASCAL benchmark, RobuMTL delivers a +2.8% average relative improvement under single perturbations and up to +44.4% under mixed weather conditions compared to the MTL baseline. On NYUD-v2, RobuMTL achieves a +9.7% average relative improvement across tasks. The code is available at GitHub.

  PublisherDOI

• FF-INT8: Efficient Forward-Forward DNN Training on Edge Devices with INT8 Precision

  ArXiv.org · 2025-06-28

  preprintOpen accessSenior author

  Backpropagation has been the cornerstone of neural network training for decades, yet its inefficiencies in time and energy consumption limit its suitability for resource-constrained edge devices. While low-precision neural network quantization has been extensively researched to speed up model inference, its application in training has been less explored. Recently, the Forward-Forward (FF) algorithm has emerged as a promising alternative to backpropagation, replacing the backward pass with an additional forward pass. By avoiding the need to store intermediate activations for backpropagation, FF can reduce memory footprint, making it well-suited for embedded devices. This paper presents an INT8 quantized training approach that leverages FF's layer-by-layer strategy to stabilize gradient quantization. Furthermore, we propose a novel "look-ahead" scheme to address limitations of FF and improve model accuracy. Experiments conducted on NVIDIA Jetson Orin Nano board demonstrate 4.6% faster training, 8.3% energy savings, and 27.0% reduction in memory usage, while maintaining competitive accuracy compared to the state-of-the-art.

  PublisherOA PDFDOI

• ChipXplore: Natural Language Exploration of Hardware Designs and Libraries

  2025-06-26

  articleSenior author

  Hardware design workflows rely on Process Design Kits (PDKs) from different fabrication nodes, each containing standard cell libraries optimized for speed, power, or density. Engineers typically navigate between the design and target PDK to make informed decisions, such as selecting gates for area optimization or enhancing the speed of the critical path. However, this process is often manual, time-consuming, and prone to errors. To address this, we present ChipXplore, a multi-agent collaborative framework powered by large language models that enables engineers to query hardware designs and PDKs using natural language. By exploiting the structured nature of PDK and hardware design data, ChipXplore retrieves relevant information through text-to-SQL and text-to-Cypher customized workflows. The framework achieves an execution accuracy of 97.39% in complex natural language queries and improves productivity by making retrieval 5.63× faster while reducing errors by 5.25× in user studies. Compared to generic workflows, ChipXplore’s customized workflow is capable of orchestrating reasoning and planning over multiple databases, improving accuracy by 29.78%. ChipXplore lays the foundation for building autonomous agents capable of tackling diverse physical design tasks that require PDK and hardware design awareness.

  PublisherDOI

• Fast Machine Learning Based Prediction for Temperature Simulation Using Compact Models

  2025-03-31 · 3 citations

  article

  As transistor densities increase, managing thermal challenges in 3D IC designs becomes more complex. Traditional methods like finite element methods and compact thermal models (CTMs) are computationally expensive, while existing machine learning (ML) models require large datasets and a long training time. To address these challenges with the ML models, we introduce a novel ML framework that integrates with CTMs to accelerate steady-state thermal simulations without needing large datasets. Our approach achieves up to 70 × speedup over state-of-the-art simulators, enabling real-time, high-resolution thermal simulations for 2D and 3D IC designs.<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>This research was partially funded by the NSF CCF 2131127 grant

  PublisherDOI

• Approximate Logic Synthesis Using BLASYS

  ArXiv.org · 2025-06-28

  preprintOpen accessSenior author

  Approximate computing is an emerging paradigm where design accuracy can be traded for improvements in design metrics such as design area and power consumption. In this work, we overview our open-source tool, BLASYS, for synthesis of approximate circuits using Boolean Matrix Factorization (BMF). In our methodology the truth table of a given circuit is approximated using BMF to a controllable approximation degree, and the results of the factorization are used to synthesize the approximate circuit output. BLASYS scales up the computations to large circuits through the use of partition techniques, where an input circuit is partitioned into a number of interconnected subcircuits and then a design-space exploration technique identifies the best order for subcircuit approximations. BLASYS leads to a graceful trade-off between accuracy and full circuit complexity as measured by design area. Using an open-source design flow, we extensively evaluate our methodology on a number of benchmarks, where we demonstrate that the proposed methodology can achieve on average 48.14% in area savings, while introducing an average relative error of 5%.

  PublisherOA PDFDOI

• HaShiFlex: A High-Throughput Hardened Shifter DNN Accelerator with Fine-Tuning Flexibility

  ArXiv.org · 2025-12-14

  preprintOpen accessSenior author

  We introduce a high-throughput neural network accelerator that embeds most network layers directly in hardware, minimizing data transfer and memory usage while preserving a degree of flexibility via a small neural processing unit for the final classification layer. By leveraging power-of-two (Po2) quantization for weights, we replace multiplications with simple rewiring, effectively reducing each convolution to a series of additions. This streamlined approach offers high-throughput, energy-efficient processing, making it highly suitable for applications where model parameters remain stable, such as continuous sensing tasks at the edge or large-scale data center deployments. Furthermore, by including a strategically chosen reprogrammable final layer, our design achieves high throughput without sacrificing fine-tuning capabilities. We implement this accelerator in a 7nm ASIC flow using MobileNetV2 as a baseline and report throughput, area, accuracy, and sensitivity to quantization and pruning - demonstrating both the advantages and potential trade-offs of the proposed architecture. We find that for MobileNetV2, we can improve inference throughput by 20x over fully programmable GPUs, processing 1.21 million images per second through a full forward pass while retaining fine-tuning flexibility. If absolutely no post-deployment fine tuning is required, this advantage increases to 67x at 4 million images per second.

  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

• CAREER: Transcending the Thermal Management Challenges of Tera-Scale Computing

  NSF · $443k · 2010–2016

• SHF: Small: Automatic High-Level Synthesis of Approximate Computing Circuits

  NSF · $458k · 2014–2018

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.