152 citations · 265 across the 8 of their papers we have counts for
9 papers
Mapping the Narrow Corridor with Large Language Models
Ebrahim M Songhori
Acemoglu and Robinson (2024 Nobel laureates in Economics) trace national histories in The Narrow Corridor as paths through a two-dimensional (state power, society power) space but…
See it to Place it: Evolving Macro Placements with Vision-Language Models
Ikechukwu Uchendu, Swati Goel, Karly Hou +5
We propose using Vision-Language Models (VLMs) for macro placement in chip floorplanning, a complex optimization task that has recently shown promising advancements through machine…
A2Perf: Real-World Autonomous Agents Benchmark
Ikechukwu Uchendu, Jason Jabbour, Korneel Van den Berghe +15
Autonomous agents and systems cover a number of application areas, from robotics and digital assistants to combinatorial optimization, all sharing common, unresolved research chall…
Delving into Macro Placement with Reinforcement Learning
Zixuan Jiang, Ebrahim Songhori, Shen Wang +5
In physical design, human designers typically place macros via trial and error, which is a Markov decision process. Reinforcement learning (RL) methods have demonstrated superhuman…
A Full-Stack Search Technique for Domain Optimized Deep Learning Accelerators
Dan Zhang, Safeen Huda, Ebrahim Songhori +4
The rapidly-changing deep learning landscape presents a unique opportunity for building inference accelerators optimized for specific datacenter-scale workloads. We propose Full-st…
Chip Placement with Deep Reinforcement Learning
Azalia Mirhoseini, Anna Goldie, Mustafa Yazgan +19
In this work, we present a learning-based approach to chip placement, one of the most complex and time-consuming stages of the chip design process. Unlike prior methods, our approa…
ARM2GC: Succinct Garbled Processor for Secure Computation
Ebrahim M. Songhori, M. Sadegh Riazi, Siam U. Hussain +2
We present ARM2GC, a novel secure computation framework based on Yao's Garbled Circuit (GC) protocol and the ARM processor. It allows users to develop privacy-preserving applicatio…
Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications
M. Sadegh Riazi, Christian Weinert, Oleksandr Tkachenko +3
We present Chameleon, a novel hybrid (mixed-protocol) framework for secure function evaluation (SFE) which enables two parties to jointly compute a function without disclosing thei…
RankMap: A Platform-Aware Framework for Distributed Learning from Dense Datasets
Azalia Mirhoseini, Eva L. Dyer, Ebrahim. M. Songhori +2
This paper introduces RankMap, a platform-aware end-to-end framework for efficient execution of a broad class of iterative learning algorithms for massive and dense datasets. Our f…