63 citations · 148 across the 7 of their papers we have counts for
10 papers
COIN: Communication-Aware In-Memory Acceleration for Graph Convolutional Networks
Sumit K. Mandal, Gokul Krishnan, A. Alper Goksoy +3
Graph convolutional networks (GCNs) have shown remarkable learning capabilities when processing graph-structured data found inherently in many application areas. GCNs distribute th…
SIAM: Chiplet-based Scalable In-Memory Acceleration with Mesh for Deep Neural Networks
Gokul Krishnan, Sumit K. Mandal, Manvitha Pannala +4
In-memory computing (IMC) on a monolithic chip for deep learning faces dramatic challenges on area, yield, and on-chip interconnection cost due to the ever-increasing model sizes.…
FLASH: Fast Neural Architecture Search with Hardware Optimization
Guihong Li, Sumit K. Mandal, Umit Y. Ogras +1
Neural architecture search (NAS) is a promising technique to design efficient and high-performance deep neural networks (DNNs). As the performance requirements of ML applications g…
Impact of On-Chip Interconnect on In-Memory Acceleration of Deep Neural Networks
Gokul Krishnan, Sumit K. Mandal, Chaitali Chakrabarti +3
With the widespread use of Deep Neural Networks (DNNs), machine learning algorithms have evolved in two diverse directions -- one with ever-increasing connection density for better…
Online Adaptive Learning for Runtime Resource Management of Heterogeneous SoCs
Sumit K. Mandal, Umit Y. Ogras, Janardhan Rao Doppa +3
Dynamic resource management has become one of the major areas of research in modern computer and communication system design due to lower power consumption and higher performance d…
Runtime Task Scheduling using Imitation Learning for Heterogeneous Many-Core Systems
Anish Krishnakumar, Samet E. Arda, A. Alper Goksoy +4
Domain-specific systems-on-chip, a class of heterogeneous many-core systems, are recognized as a key approach to narrow down the performance and energy-efficiency gap between custo…