16 citations · 69 across the 23 of their papers we have counts for
12 papers · 1 filter
Architecture, Dataflow and Physical Design Implications of 3D-ICs for DNN-Accelerators
Jan Moritz Joseph, Ananda Samajdar, Lingjun Zhu +4
The everlasting demand for higher computing power for deep neural networks (DNNs) drives the development of parallel computing architectures. 3D integration, in which chips are int…
Dataflow-Architecture Co-Design for 2.5D DNN Accelerators using Wireless Network-on-Package
Robert Guirado, Hyoukjun Kwon, Sergi Abadal +2
Deep neural network (DNN) models continue to grow in size and complexity, demanding higher computational power to enable real-time inference. To efficiently deliver such computatio…
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…