4 papers
A complete discussion on fully reconfigurable, digital, scalable, graph and sparsity-aware near-memory accelerator for graph neural networks
Siddhartha Raman Sundara Raman, Lizy John, Jaydeep P. Kulkarni
Graph neural networks (GNNs) have gained significant interest for applications such as citation network analysis and drug discovery due to their ability to apply machine learning t…
A comprehensive study on ILP acceleration accounting for sparsity, area, energy, data movement using near-memory architecture
Siddhartha Raman Sundara Raman, Lizy K John, Jaydeep P. Kulkarni
Integer Linear Programming (ILP) is widely used for solving real-world optimization problems, including network routing, map routing, and traffic scheduling. However, ILP algorithm…
A detailed algorithmic study on a reuse-aware, near memory, all-digital Ising machine
Siddhartha Raman Sundara Raman, Lizy K. John, Jaydeep P. Kulkarni
Recently, nature-inspired computing approaches have gained significant attention for solving difficult optimization problems, particularly through Ising machines for NP-complete ap…
ABI: A tightly integrated, unified, sparsity-aware, reconfigurable, compute near-register file/cache GPU architecture with light-weight softmax for deep learning, linear algebra, and Ising compute
Siddhartha Raman Sundara Raman, Jaydeep P. Kulkarni
We present a tightly integrated and unified near-memory GPU architecture that delivers 6 to 16 times speedup and 6 to 13 times energy savings across Convolutional Neural Networks,…