10 citations · 24 across the 9 of their papers we have counts for
10 papers
Efficient Sparse Processing-in-Memory Architecture (ESPIM) for Machine Learning Inference
Mingxuan He, Mithuna Thottethodi, T. N. Vijaykumar
Emerging machine learning (ML) models (e.g., transformers) involve memory pin bandwidth-bound matrix-vector (MV) computation in inference. By avoiding pin crossings, processing in…
QED: Scalable Verification of Hardware Memory Consistency
Gokulan Ravi, Xiaokang Qiu, Mithuna Thottethodi +1
Memory consistency model (MCM) issues in out-of-order-issue microprocessor-based shared-memory systems are notoriously non-intuitive and a source of hardware design bugs. Prior har…
SafeBet: Secure, Simple, and Fast Speculative Execution
Conor Green, Cole Nelson, Mithuna Thottethodi +1
Spectre attacks exploit microprocessor speculative execution to read and transmit forbidden data outside the attacker's trust domain and sandbox. Recent hardware schemes allow pote…
OCCAM: Optimal Data Reuse for Convolutional Neural Networks
Ashish Gondimalla, Jianqiao Liu, T. N. Vijaykumar +1
Convolutional neural networks (CNNs) are emerging as powerful tools for image processing in important commercial applications. We focus on the important problem of improving the la…
Barrier-Free Large-Scale Sparse Tensor Accelerator (BARISTA) For Convolutional Neural Networks
Ashish Gondimalla, Sree Charan Gundabolu, T. N. Vijaykumar +1
Convolutional neural networks (CNNs) are emerging as powerful tools for visual recognition. Recent architecture proposals for sparse CNNs exploit zeros in the feature maps and filt…
Booster: An Accelerator for Gradient Boosting Decision Trees
Mingxuan He, T. N. Vijaykumar, Mithuna Thottethodi
We propose Booster, a novel accelerator for gradient boosting trees based on the unique characteristics of gradient boosting models. We observe that the dominant steps of gradient…