PPT-Approximate Associative Memristive Memory for Energy-Efficient GPUs

Author : briana-ranney | Published Date : 2019-01-30

Abbas Rahimi Amirali Ghofrani KwangTing Cheng Luca Benini Rajesh K Gupta UC San Diego UC Santa Barbara ETH Zurich NSF Variability Expedition ERC MultiTherman

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Approximate Associative Memristive Memory for Energy-Efficient GPUs: Transcript

Abbas Rahimi Amirali Ghofrani KwangTing Cheng Luca Benini Rajesh K Gupta UC San Diego UC Santa Barbara ETH Zurich NSF Variability Expedition ERC MultiTherman Motivation Energy Efficiency in GPUs. : . 인식. Associative computer: a hybrid . connectionistic. production system. Action Editor : John . Barnden. 발제 . : . 최 봉환. , 04/07, 2009. Outline. Introduce Associative computer. = "a . FAWN. :. Workloads and Implications. Vijay . Vasudevan. , David Andersen, Michael . Kaminsky. *, Lawrence Tan, . Jason Franklin. , . Iulian. . Moraru. Carnegie Mellon University, *Intel Labs Pittsburgh. Abbas Rahimi, . Andrea . Marongiu. ,. . Rajesh K. Gupta, Luca . Benini. UC San Diego, and . University of Bologna . Micrel.deis.unibo.it. /. MultiTherman. variability.org. Outline. Introduction. and . Using Brain-Inspired . Hyperdimensional Computing. Abbas . Rahimi. , . Pentti. . Kanerva. , Jan M. . Rabaey. UC Berkeley. Outline. Background in HD Computing. Language Recognition as an Example. HD Memory-centric Architecture. Abbas Rahimi, . Andrea . Marongiu. ,. . Rajesh K. Gupta, Luca . Benini. UC San Diego, and . University of Bologna . Micrel.deis.unibo.it. /. MultiTherman. variability.org. Outline. Introduction. and . Betkaoui, B.; Thomas, D.B.; Luk, W., "Comparing performance and energy efficiency of FPGAs and GPUs for high productivity computing," . Field-Programmable Technology (FPT), 2010 International Conference on. Mohsen . Imani, . Abbas . Rahimi. , . Deqian. . Kong, . Tajana. . Rosing. , . and Jan M. . Rabaey. CSE Department, UC San Diego. EECS Department, UC Berkeley. . Outline. Background in HD . Computing. *University . of California . Berkeley. Mohsen Imani, . Abbas . Rahimi. *. , . Tajana S. Rosing. Resistive Conﬁgurable Associative Memory . for Approximate . Computing. Motivation. 2. Internet of Things. Abbas Rahimi, . Andrea . Marongiu. ,. . Rajesh K. Gupta, Luca . Benini. UC San Diego, and . University of Bologna . Micrel.deis.unibo.it. /. MultiTherman. variability.org. Outline. Introduction. and . Memristive. Switches. Abstract. This work explores anti-serial (anti-parallel) . memristive. switches—ASMs (APMs)—as potential cross-point elements in . nano. -crossbar resistive random access memory arrays. The memory operation principles for both device combinations are shown in detail. The effectiveness of these . FAWN. :. Workloads and Implications. Vijay . Vasudevan. , David Andersen, Michael . Kaminsky. *, Lawrence Tan, . Jason Franklin. , . Iulian. . Moraru. Carnegie Mellon University, *Intel Labs Pittsburgh. Neural Networks Using Approximate. Multiply-Accumulate Blocks. Mohammadreza Esmali . Nojehdeh. , . Levent Aksoy. . and Mustafa Altun. Emerging Circuits and Computation (ECC) Group. Istanbul Technical University. Episodic retrieval of visually rich items and associations in young and older adults: Evidence from ERPs. Introduction . I. Item and Associative Encoding Tasks. III. Item and Associative Recognition Tasks. April . 4. th. 2019. Desiderata for memory models. Search. To explain list-length and fan effects. Direct access. To explain rapid true negatives in recognition. Implicit recognition. To explain the mind’s solution to the correspondence problem.

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