43 citations · 49 across the 6 of their papers we have counts for
6 papers
Restoring the Broken Covenant Between Compilers and Deep Learning Accelerators
Sean Kinzer, Soroush Ghodrati, Rohan Mahapatra +6
Deep learning accelerators address the computational demands of Deep Neural Networks (DNNs), departing from the traditional Von Neumann execution model. They leverage specialized h…
An Open-Source ML-Based Full-Stack Optimization Framework for Machine Learning Accelerators
Hadi Esmaeilzadeh, Soroush Ghodrati, Andrew B. Kahng +8
Parameterizable machine learning (ML) accelerators are the product of recent breakthroughs in ML. To fully enable their design space exploration (DSE), we propose a physical-design…
Performance Analysis of DNN Inference/Training with Convolution and non-Convolution Operations
Hadi Esmaeilzadeh, Soroush Ghodrati, Andrew B. Kahng +4
Today's performance analysis frameworks for deep learning accelerators suffer from two significant limitations. First, although modern convolutional neural network (CNNs) consist o…
In-Storage Domain-Specific Acceleration for Serverless Computing
Rohan Mahapatra, Soroush Ghodrati, Byung Hoon Ahn +8
While (1) serverless computing is emerging as a popular form of cloud execution, datacenters are going through major changes: (2) storage dissaggregation in the system infrastructu…
Mixed-Signal Charge-Domain Acceleration of Deep Neural networks through Interleaved Bit-Partitioned Arithmetic
Soroush Ghodrati, Hardik Sharma, Sean Kinzer +5
Low-power potential of mixed-signal design makes it an alluring option to accelerate Deep Neural Networks (DNNs). However, mixed-signal circuitry suffers from limited range for inf…
ReLeQ: A Reinforcement Learning Approach for Deep Quantization of Neural Networks
Ahmed T. Elthakeb, Prannoy Pilligundla, FatemehSadat Mireshghallah +2
Deep Neural Networks (DNNs) typically require massive amount of computation resource in inference tasks for computer vision applications. Quantization can significantly reduce DNN…