An Open-Source ML-Based Full-Stack Optimization Framework for Machine Learning Accelerators

1 code implementation • 23 Aug 2023 • Hadi Esmaeilzadeh, Soroush Ghodrati, Andrew B. Kahng, Joon Kyung Kim, Sean Kinzer, Sayak Kundu, Rohan Mahapatra, Susmita Dey Manasi, Sachin Sapatnekar, Zhiang Wang, Ziqing Zeng

Parameterizable machine learning (ML) accelerators are the product of recent breakthroughs in ML.

Performance Analysis of DNN Inference/Training with Convolution and non-Convolution Operations

no code implementations • 29 Jun 2023 • Hadi Esmaeilzadeh, Soroush Ghodrati, Andrew B. Kahng, Sean Kinzer, Susmita Dey Manasi, Sachin S. Sapatnekar, Zhiang Wang

The modeling effort of SimDIT comprehensively covers convolution and non-convolution operations of both CNN inference and training on a highly parameterizable hardware substrate.

Mixed-Signal Charge-Domain Acceleration of Deep Neural networks through Interleaved Bit-Partitioned Arithmetic

no code implementations • 27 Jun 2019 • Soroush Ghodrati, Hardik Sharma, Sean Kinzer, Amir Yazdanbakhsh, Kambiz Samadi, Nam Sung Kim, Doug Burger, Hadi Esmaeilzadeh

Low-power potential of mixed-signal design makes it an alluring option to accelerate Deep Neural Networks (DNNs).

Hardware Architecture