Zobrazeno 1 - 10
of 77
pro vyhledávání: '"Thottethodi, Mithuna"'
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 memory (PIM) can improve performance and energy for pin-bound workload
Externí odkaz:
http://arxiv.org/abs/2404.04708
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 hardware verification work is limited to in-order-issue processors, to pr
Externí odkaz:
http://arxiv.org/abs/2404.03113
Autor:
Green, Conor, Thottethodi, Mithuna
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 network designs that balance the competing goals of performance requiremen
Externí odkaz:
http://arxiv.org/abs/2404.02357
Spectre attacks exploit microprocessor speculative execution to read and transmit forbidden data outside the attacker's trust domain and sandbox. Recent hardware schemes allow potentially-unsafe speculative accesses but prevent the secret's transmiss
Externí odkaz:
http://arxiv.org/abs/2306.07785
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 latency of image recognition. CNNs' large data at each layer's input, fi
Externí odkaz:
http://arxiv.org/abs/2106.14138
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 filters for performance and energy without losing accuracy. Sparse archite
Externí odkaz:
http://arxiv.org/abs/2104.08734
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 boosting training (accounting for 90-98% of training time) involve sim
Externí odkaz:
http://arxiv.org/abs/2011.02022
Autor:
Xue, Jiachen, Chaudhry, Muhammad Usama, Vamanan, Balajee, Vijaykumar, T. N., Thottethodi, Mithuna
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 of incasts is a challenge. Targeting the full generality of the congest
Externí odkaz:
http://arxiv.org/abs/1805.11158
Enabling Efficient Dynamic Resizing of Large DRAM Caches via A Hardware Consistent Hashing Mechanism
Autor:
Chang, Kevin K., Loh, Gabriel H., Thottethodi, Mithuna, Eckert, Yasuko, O'Connor, Mike, Manne, Srilatha, Hsu, Lisa, Subramanian, Lavanya, Mutlu, Onur
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 productively utilize this much cache space, however. Unfortunately, the unu
Externí odkaz:
http://arxiv.org/abs/1602.00722
Publikováno v:
In Journal of Parallel and Distributed Computing May 2013 73(5):608-620
