16 citations · 61 across the 12 of their papers we have counts for
14 papers
ConfuciuX: Autonomous Hardware Resource Assignment for DNN Accelerators using Reinforcement Learning
Sheng-Chun Kao, Geonhwa Jeong, Tushar Krishna
DNN accelerators provide efficiency by leveraging reuse of activations/weights/outputs during the DNN computations to reduce data movement from DRAM to the chip. The reuse is captu…
CLAN: Continuous Learning using Asynchronous Neuroevolution on Commodity Edge Devices
Parth Mannan, Ananda Samajdar, Tushar Krishna
Recent advancements in machine learning algorithms, especially the development of Deep Neural Networks (DNNs) have transformed the landscape of Artificial Intelligence (AI). With e…
Restructuring, Pruning, and Adjustment of Deep Models for Parallel Distributed Inference
Afshin Abdi, Saeed Rashidi, Faramarz Fekri +1
Using multiple nodes and parallel computing algorithms has become a principal tool to improve training and execution times of deep neural networks as well as effective collective i…
Breaking Barriers: Maximizing Array Utilization for Compute In-Memory Fabrics
Brian Crafton, Samuel Spetalnick, Gauthaman Murali +3
Compute in-memory (CIM) is a promising technique that minimizes data transport, the primary performance bottleneck and energy cost of most data intensive applications. This has fou…
Generative Design of Hardware-aware DNNs
Sheng-Chun Kao, Arun Ramamurthy, Tushar Krishna
To efficiently run DNNs on the edge/cloud, many new DNN inference accelerators are being designed and deployed frequently. To enhance the resource efficiency of DNNs, model quantiz…
The gem5 Simulator: Version 20.0+
Jason Lowe-Power, Abdul Mutaal Ahmad, Ayaz Akram +75
The open-source and community-supported gem5 simulator is one of the most popular tools for computer architecture research. This simulation infrastructure allows researchers to mod…