Zobrazeno 1 - 10
of 53
pro vyhledávání: '"ZABIHI, MASOUD"'
Autor:
Xie, Yanyue, Dong, Peiyan, Yuan, Geng, Li, Zhengang, Zabihi, Masoud, Wu, Chao, Chang, Sung-En, Zhang, Xufeng, Lin, Xue, Ding, Caiwen, Yoshikawa, Nobuyuki, Chen, Olivia, Wang, Yanzhi
Superconducting circuits, like Adiabatic Quantum-Flux-Parametron (AQFP), offer exceptional energy efficiency but face challenges in physical design due to sophisticated spacing and timing constraints. Current design tools often neglect the importance
Externí odkaz:
http://arxiv.org/abs/2407.18209
Autor:
Zabihi, Masoud, Xie, Yanyue, Li, Zhengang, Dong, Peiyan, Yuan, Geng, Chen, Olivia, Pedram, Massoud, Wang, Yanzhi
The production process of superconductive integrated circuits is complex and consumes significant amounts of resources and energy. Therefore, it is crucial to evaluate the environmental impact of this emerging technology. An attractive option for the
Externí odkaz:
http://arxiv.org/abs/2307.12216
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:
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:
Resch, Salonik, Khatamifard, S. Karen, Chowdhury, Zamshed Iqbal, Zabihi, Masoud, Zhao, Zhengyang, Wang, Jian-Ping, Sapatnekar, Sachin S., Karpuzcu, Ulya R.
There is increasing demand to bring machine learning capabilities to low power devices. By integrating the computational power of machine learning with the deployment capabilities of low power devices, a number of new applications become possible. In
Externí odkaz:
http://arxiv.org/abs/1908.11373
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:
Resch, Salonik, Khatamifard, S. Karen, Chowdhury, Zamshed Iqbal, Zabihi, Masoud, Zhao, Zhengyang, Wang, Jian-Ping, Sapatnekar, Sachin S., Karpuzcu, Ulya R.
Neural networks span a wide range of applications of industrial and commercial significance. Binary neural networks (BNN) are particularly effective in trading accuracy for performance, energy efficiency or hardware/software complexity. Here, we intr
Externí odkaz:
http://arxiv.org/abs/1812.03989
Autor:
Vahidnezhad, Hassan, Youssefian, Leila, Saeidian, Amir Hossein, Boyden, Lynn M, Touati, Andrew, Harvey, Nailah, Naji, Mahtab, Zabihi, Masoud, Barzegar, Mohammadreza, Sotoudeh, Soheila, Liu, Lu, Guy, Alyson, Kariminejad, Ariana, Zeinali, Sirous, Choate, Keith A, McGrath, John A., Uitto, Jouni
Publikováno v:
In Matrix Biology May 2021 99:43-57
