5 citations · 6 across the 9 of their papers we have counts for
10 papers
Throughput-Optimized Networks at Scale
Conor Green, Conor James Green, Mithuna Thottethodi +1
Data center network design plays a critical role in AI training by supporting scaling to thousands of accelerators. An open problem, designing a near-optimal throughput-oriented ne…
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…
NetSmith: An Optimization Framework for Machine-Discovered Network Topologies
Conor Green, Mithuna Thottethodi
Over the past few decades, network topology design for general purpose, shared memory multicores has been primarily driven by human experts who use their insights to arrive at netw…
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…
Dart: Divide and Specialize for Fast Response to Congestion in RDMA-based Datacenter Networks
Jiachen Xue, Muhammad Usama Chaudhry, Balajee Vamanan +2
Though Remote Direct Memory Access (RDMA) promises to reduce datacenter network latencies significantly compared to TCP (e.g., 10x), end-to-end congestion control in the presence o…
Enabling Efficient Dynamic Resizing of Large DRAM Caches via A Hardware Consistent Hashing Mechanism
Kevin K. Chang, Gabriel H. Loh, Mithuna Thottethodi +6
Die-stacked DRAM has been proposed for use as a large, high-bandwidth, last-level cache with hundreds or thousands of megabytes of capacity. Not all workloads (or phases) can produ…