50 citations · 113 across the 17 of their papers we have counts for
18 papers
MINISA: Minimal Instruction Set Architecture for Next-gen Reconfigurable Inference Accelerator
Jianming Tong, Devansh Jain, Yujie Li +2
Modern reconfigurable AI accelerators rely on rich mapping and data-layout flexibility to sustain high utilization across matrix multiplication, convolution, and emerging applicati…
TensorRight: Automated Verification of Tensor Graph Rewrites
Jai Arora, Sirui Lu, Devansh Jain +9
Tensor compilers, essential for generating efficient code for deep learning models across various applications, employ tensor graph rewrites as one of the key optimizations. These…
Automatically Generating ML Compiler Backends from Tensor Accelerator ISA Descriptions
Devansh Jain, Akash Pardeshi, Marco Frigo +5
Machine learning (ML) compilers play a key role in enabling high-performance implementations of ML workloads. These compilers use existing CPU and GPU backends to generate device-s…
COGNATE: Acceleration of Sparse Tensor Programs on Emerging Hardware using Transfer Learning
Chamika Sudusinghe, Gerasimos Gerogiannis, Damitha Lenadora +3
Sparse tensor programs are essential in deep learning and graph analytics, driving the need for optimized processing. To meet this demand, specialized hardware accelerators are bei…
Transforming the Hybrid Cloud for Emerging AI Workloads
Deming Chen, Alaa Youssef, Ruchi Pendse +42
This white paper, developed through close collaboration between IBM Research and UIUC researchers within the IIDAI Institute, envisions transforming hybrid cloud systems to meet th…
SPLAT: A framework for optimised GPU code-generation for SParse reguLar ATtention
Ahan Gupta, Yueming Yuan, Devansh Jain +4
Multi-head-self-attention (MHSA) mechanisms achieve state-of-the-art (SOTA) performance across natural language processing and vision tasks. However, their quadratic dependence on…