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Title:
memristors for neural branch prediction: a case study in strict latency and write endurance challenges
Author: Saadeldeen Hebatallah ; Franklin Diana ; Long Guoping ; Hill Charlotte ; Browne Aisha ; Strukov Dmitri ; Sherwood Timothy ; Chong Frederic T.
Source: Proceedings of the ACM International Conference on Computing Frontiers, CF 2013
Conference Name: 2013 ACM International Conference on Computing Frontiers, CF 2013
Conference Date: May 14, 2013 - May 16, 2013
Issued Date: 2013
Conference Place: Ischia, Italy
Keyword: Analog computers ; Hybrid systems ; Memristors ; Neural networks ; Passive filters ; Reliability ; Static random access storage
Indexed Type: EI
ISBN: 9781450320535
Department: (1) Department of Computer Science UC Santa Barbara United States; (2) Electrical and Computer Engineering UC Santa Barbara United States; (3) Institute of Software Chinese Academy of Sciences China
Sponsorship: ACM SIGMICRO
Abstract: Memristors offer many potential advantages over more traditional memory-cell technologies, including the potential for extreme densities, and fast read times. Current devices, however, are plagued by problems of yield, and durability. We present a limit study of an aggressive neural network application that has a high update rate and a strict latency requirement, analog neural branch predictor. Of course, traditional analog neural network (ANN) implementations of branch predictors are not built with the idea that the underlying bits are likely to fail due to both manufacturing and wear-out issues. Without some careful precautions, a direct one-to-one replacement will result in poor behavior. We propose a hybrid system that uses SRAM front-end cache, and a distributed-sum scheme to overcome memristors' limitations. Our design can leverage devices with even modest durability (surviving only hours of continuous switching) to provide a system lasting 5 or more years of continuous operation. In addition, these schemes allow for a fault-tolerant design as well. We find that, while a neural predictor benefits from larger density, current technology parameters do not allow high dense, energy-efficient design. Thus, we discuss a range of plausible memristor characteristics that would; as the technology advances; make them practical for our application. Copyright 2013 ACM.
English Abstract: Memristors offer many potential advantages over more traditional memory-cell technologies, including the potential for extreme densities, and fast read times. Current devices, however, are plagued by problems of yield, and durability. We present a limit study of an aggressive neural network application that has a high update rate and a strict latency requirement, analog neural branch predictor. Of course, traditional analog neural network (ANN) implementations of branch predictors are not built with the idea that the underlying bits are likely to fail due to both manufacturing and wear-out issues. Without some careful precautions, a direct one-to-one replacement will result in poor behavior. We propose a hybrid system that uses SRAM front-end cache, and a distributed-sum scheme to overcome memristors' limitations. Our design can leverage devices with even modest durability (surviving only hours of continuous switching) to provide a system lasting 5 or more years of continuous operation. In addition, these schemes allow for a fault-tolerant design as well. We find that, while a neural predictor benefits from larger density, current technology parameters do not allow high dense, energy-efficient design. Thus, we discuss a range of plausible memristor characteristics that would; as the technology advances; make them practical for our application. Copyright 2013 ACM.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/15973
Appears in Collections:软件所图书馆_会议论文

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Recommended Citation:
Saadeldeen Hebatallah,Franklin Diana,Long Guoping,et al. memristors for neural branch prediction: a case study in strict latency and write endurance challenges[C]. 见:2013 ACM International Conference on Computing Frontiers, CF 2013. Ischia, Italy. May 14, 2013 - May 16, 2013.
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