Zobrazeno 1 - 10
of 20
pro vyhledávání: '"CHOWDHURY, ZAMSHED I."'
Autor:
Cılasun, Hüsrev, Resch, Salonik, Chowdhury, Zamshed I., Zabihi, Masoud, Lv, Yang, Zink, Brandon, Wang, Jian-Ping, Sapatnekar, Sachin S., Karpuzcu, Ulya R.
Publikováno v:
2024 ACM/IEEE 51st Annual International Symposium on Computer Architecture (ISCA)
Processing in memory (PiM) represents a promising computing paradigm to enhance performance of numerous data-intensive applications. Variants performing computing directly in emerging nonvolatile memories can deliver very high energy efficiency. PiM
Externí odkaz:
http://arxiv.org/abs/2207.13261
Autor:
Resch, Salonik, Chowdhury, Zamshed I., Cilasun, Husrev, Zabihi, Masoud, Zhao, Zhengyang, Wang, Jian-Ping, Sapatnekar, Sachin, Karpuzcu, Ulya R.
Beyond edge devices can function off the power grid and without batteries, enabling them to operate in difficult to access regions. However, energy costly long-distance communication required for reporting results or offloading computation becomes a
Externí odkaz:
http://arxiv.org/abs/2112.08943
Autor:
Zabihi, Masoud, Resch, Salonik, Cılasun, Husrev, Chowdhury, Zamshed I., Zhao, Zhengyang, Karpuzcu, Ulya R., Wang, Jian-Ping, Sapatnekar, Sachin S.
This paper describes how 3D XPoint memory arrays can be used as in-memory computing accelerators. We first show that thresholded matrix-vector multiplication (TMVM), the fundamental computational kernel in many applications including machine learning
Externí odkaz:
http://arxiv.org/abs/2106.08402
Autor:
Sakalis, Christos, Chowdhury, Zamshed I., Wadle, Shayne, Akturk, Ismail, Ros, Alberto, Själander, Magnus, Kaxiras, Stefanos, Karpuzcu, Ulya R.
Recent architectural approaches that address speculative side-channel attacks aim to prevent software from exposing the microarchitectural state changes of transient execution. The Delay-on-Miss technique is one such approach, which simply delays loa
Externí odkaz:
http://arxiv.org/abs/2102.10932
Autor:
Cılasun, Hüsrev, Resch, Salonik, Chowdhury, Zamshed I., Olson, Erin, Zabihi, Masoud, Zhao, Zhengyang, Peterson, Thomas, Parhi, Keshab, Wang, Jian-Ping, Sapatnekar, Sachin S., Karpuzcu, Ulya
Publikováno v:
ACM Transactions on Architecture and Code Optimization Volume 18 Issue 4 December 2021 Article No.: 59
Spiking Neural Networks (SNN) represent a biologically inspired computation model capable of emulating neural computation in human brain and brain-like structures. The main promise is very low energy consumption. Unfortunately, classic Von Neumann ar
Externí odkaz:
http://arxiv.org/abs/2006.03007
Autor:
Chowdhury, Zamshed I., Khatamifard, S. Karen, Zhao, Zhengyang, Zabihi, Masoud, Resch, Salonik, Razaviyayn, Meisam, Wang, Jian-Ping, Sapatnekar, Sachin, Karpuzcu, Ulya R.
Traditional Von Neumann computing is falling apart in the era of exploding data volumes as the overhead of data transfer becomes forbidding. Instead, it is more energy-efficient to fuse compute capability with memory where the data reside. This is pa
Externí odkaz:
http://arxiv.org/abs/1812.08918
Autor:
Zenia, Nusrat Zerin, Aseeri, Mohammed, Ahmed, Muhammad R., Chowdhury, Zamshed I., Shamim Kaiser, M.
Publikováno v:
In Journal of Network and Computer Applications August 2016 71:72-85
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