Search Results - "2019 ACM/IEEE 46th Annual International Symposium on Computer Architecture (ISCA)"

FloatPIM: In-Memory Acceleration of Deep Neural Network Training with High Precision by Imani, Mohsen, Gupta, Saransh, Kim, Yeseong, Rosing, Tajana

“…Processing In-Memory (PIM) has shown a great potential to accelerate inference tasks of Convolutional Neural Network (CNN). However, existing PIM architectures…”
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Asymptotic Improvements to Quantum Circuits via Qutrits by Gokhale, Pranav, Baker, Jonathan M., Duckering, Casey, Brown, Natalie C., Brown, Kenneth R., Chong, Frederic T.

“…Quantum computation is traditionally expressed in terms of quantum bits, or qubits. In this work, we instead consider three-level qutrits. Past work with…”
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Sparse ReRAM Engine: Joint Exploration of Activation and Weight Sparsity in Compressed Neural Networks by Yang, Tzu-Hsien, Cheng, Hsiang-Yun, Yang, Chia-Lin, Tseng, I-Ching, Hu, Han-Wen, Chang, Hung-Sheng, Li, Hsiang-Pang

“…Exploiting model sparsity to reduce ineffectual computation is a commonly used approach to achieve energy efficiency for DNN inference accelerators. However,…”
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CoNDA: Efficient Cache Coherence Support for Near-Data Accelerators by Boroumand, Amirali, Zheng, Hongzhong, Mutlu, Onur, Ghose, Saugata, Patel, Minesh, Hassan, Hasan, Lucia, Brandon, Ausavarungnirun, Rachata, Hsieh, Kevin, Hajinazar, Nastaran, Malladi, Krishna T.

“…Specialized on-chip accelerators are widely used to improve the energy efficiency of computing systems. Recent advances in memory technology have enabled…”
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Statistical Assertions for Validating Patterns and Finding Bugs in Quantum Programs by Huang, Yipeng, Martonosi, Margaret

“…In support of the growing interest in quantum computing experimentation, programmers need new tools to write quantum algorithms as program code. Compared to…”
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New Attacks and Defense for Encrypted-Address Cache by Qureshi, Moinuddin K.

“…Conflict-based cache attacks can allow an adversary to infer the access pattern of a co-running application by orchestrating evictions via cache conflicts…”
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Accelerating Distributed Reinforcement learning with In-Switch Computing by Li, Youjie, Liu, Iou-Jen, Yuan, Yifan, Chen, Deming, Schwing, Alexander, Huang, Jian

“…Reinforcement learning (RL) has attracted much attention recently, as new and emerging AI-based applications are demanding the capabilities to intelligently…”
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CROW: A Low-Cost Substrate for Improving DRAM Performance, Energy Efficiency, and Reliability by Hassan, Hasan, Patel, Minesh, Kim, Jeremie S., Yaglikci, A. Giray, Vijaykumar, Nandita, Ghiasi, Nika Mansouri, Ghose, Saugata, Mutlu, Onur

“…DRAM has been the dominant technology for architecting main memory for decades. Recent trends in multi-core system design and large-dataset applications have…”
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Efficient Invisible Speculative Execution through Selective Delay and Value Prediction by Sakalis, Christos, Kaxiras, Stefanos, Ros, Alberto, Jimborean, Alexandra, Sjalander, Magnus

“…Speculative execution, the base on which modern high-performance general-purpose CPUs are built on, has recently been shown to enable a slew of security…”
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Full-Stack, Real-System Quantum Computer Studies: Architectural Comparisons and Design Insights by Murali, Prakash, Linke, Norbert Matthias, Martonosi, Margaret, Abhari, Ali Javadi, Nguyen, Nhung Hong, Alderete, Cinthia Huerta

“…In recent years, Quantum Computing (QC) has progressed to the point where small working prototypes are available for use. Termed Noisy Intermediate-Scale…”
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MGPUSim: Enabling Multi-GPU Performance Modeling and Optimization by Sun, Yifan, Zhao, Vincent, Barclay, Harrison, Ziabari, Amir Kavyan, Chen, Zhongliang, Ubal, Rafael, Abellan, Jose L., Kim, John, Joshi, Ajay, Kaeli, David, Baruah, Trinayan, Mojumder, Saiful A., Dong, Shi, Gong, Xiang, Treadway, Shane, Bao, Yuhui, Hance, Spencer, McCardwell, Carter

“…The rapidly growing popularity and scale of data-parallel workloads demand a corresponding increase in raw computational power of Graphics Processing Units…”
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Eager Pruning: Algorithm and Architecture Support for Fast Training of Deep Neural Networks by Zhang, Jiaqi, Chen, Xiangru, Song, Mingcong, Li, Tao

“…Today's big and fast data and the changing circumstance require fast training of Deep Neural Networks (DNN) in various applications. However, training a DNN…”
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SoftSKU: Optimizing Server Architectures for Microservice Diversity @Scale by Sriraman, Akshitha, Dhanotia, Abhishek, Wenisch, Thomas F.

“…The variety and complexity of microservices in warehouse- scale data centers has grown precipitously over the last few years to support a growing user base and…”
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Laconic Deep Learning Inference Acceleration by Sharify, Sayeh, Lascorz, Alberto Delmas, Mahmoud, Mostafa, Nikolic, Milos, Siu, Kevin, Stuart, Dylan Malone, Poulos, Zissis, Moshovos, Andreas

“…We present a method for transparently identifying ineffectual computations during inference with Deep Learning models. Specifically, by decomposing…”
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TWiCe: Preventing Row-hammering by Exploiting Time Window Counters by Lee, Eojin, Kang, Ingab, Lee, Sukhan, Suh, G. Edward, Ahn, Jung Ho

“…Computer systems using DRAM are exposed to row-hammer (RH) attacks, which can flip data in a DRAM row without directly accessing a row but by frequently…”
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MnnFast: A Fast and Scalable System Architecture for Memory-Augmented Neural Networks by Jang, Hanhwi, Kim, Joonsung, Jo, Jae-Eon, Lee, Jaewon, Kim, Jangwoo

“…Memory-augmented neural networks are getting more attention from many researchers as they can make an inference with the previous history stored in memory…”
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Interplay between Hardware Prefetcher and Page Eviction Policy in CPU-GPU Unified Virtual Memory by Ganguly, Debashis, Zhang, Ziyu, Yang, Jun, Melhem, Rami

“…Memory capacity in GPGPUs is a major challenge for data-intensive applications with their ever increasing memory requirement. To fit a workload into the…”
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TIE: Energy-efficient Tensor Train-based Inference Engine for Deep Neural Network by Deng, Chunhua, Sun, Fangxuan, Qian, Xuehai, Lin, Jun, Wang, Zhongfeng, Yuan, Bo

“…In the era of artificial intelligence (AI), deep neural networks (DNNs) have emerged as the most important and powerful AI technique. However, large DNN models…”
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AsmDB: Understanding and Mitigating Front-End Stalls in Warehouse-Scale Computers by Ayers, Grant, Ranganathan, Parthasarathy, Nagendra, Nayana Prasad, August, David I., Cho, Hyoun Kyu, Kanev, Svilen, Kozyrakis, Christos, Krishnamurthy, Trivikram, Litz, Heiner, Moseley, Tipp

“…The large instruction working sets of private and public cloud workloads lead to frequent instruction cache misses and costs in the millions of dollars. While…”
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DeepAttest: An End-to-End Attestation Framework for Deep Neural Networks by Chen, Huili, Fu, Cheng, Rouhani, Bita Darvish, Zhao, Jishen, Koushanfar, Farinaz

“…Emerging hardware architectures for Deep Neural Networks (DNNs) are being commercialized and considered as the hardware- level Intellectual Property (IP) of…”
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