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Yao Z, Dong Z, Zheng Z, Gholami A, Yu J, Tan E, Wang L, Huang Q, Wang Y, Mahoney MW, Keutzer K. HAWQV3: Dyadic Neural Network Quantization. arXiv preprint arXiv:2011.10680. 2020 Nov 20. Singh G, Chelini L, Corda S, et al. Near-memory computing: past, present, and future. Microprocessors Microsyst, 2019, 71: 102868
Ahn J, Hong S, Yoo S, et al. A scalable processing-in-memory accelerator for parallel graph processing. In: Proceedings of 2015 ACM/IEEE 42nd Annual International Symposium on Computer Architecture (ISCA), Portland, 2015. 105–117 Stokes J (2008) Analysis: more than 16 cores may well be pointless. In: Ars Technica. Condé Nast Digital. Available http://arstechnica.com/hardware/news/2008/12/analysis-more-than-16-cores-may-well-be-pointless.ars, Dec. 2008The Memory Wall isn’t a game of strategy, but of appreciation. The only rule is that players should recall and draw positive, uplifting memories—nothing offensive or negative. And there is a general guideline about drawing the memory scenes: players should be discouraged from judging their drawings or the drawings of others. Tell them that the activity is designed to share anecdotes and stories—not win a drawing contest. The images are there to illustrate the scenes and, absolutely, to provide good-natured humor. In the other stories, too, Doerr moves with grace between the larger rhythms of the natural world and the closed worlds of individual consciousness. In "Procreate, Generate", a couple struggle to conceive as the seasons turn; the wryly conversational, heartbreaking "The River Nemunas" sees an American teenage orphan start a new life in Lithuania; in "Village 123", a Chinese seed keeper anticipates the submersion of her village as part of a new dam project. This last story falls victim to the normally fastidious Doerr's weakness for overexplicitness: memory, we are told sternly and repeatedly, is a seed – "it is a village slated to be inundated". The cost constraints of memory bandwidth and capacity show up in Nvidia’s A100 GPUs constantly. The A100 tends to have very low FLOPS utilization without heavy optimization. FLOPS utilization measures the total computed FLOPS required to train a model vs. the theoretical FLOPS the GPUs could compute in a model’s training time.
The impetus of a Doerr story is always a movement toward transcendence… Doerr writes about the big questions, the imponderables, the major metaphysical dreads, and he does it fearlessly.” Ahmed Amine Jerraya and Wayne Wolf (2005). Multiprocessor Systems-on-chips. Morgan Kaufmann. pp.90–91. ISBN 9780123852519. Archived from the original on August 1, 2016 . Retrieved March 31, 2014. The Emergence of Practical MRAM "Crocus Technology | Magnetic Sensors | TMR Sensors" (PDF). Archived from the original (PDF) on 2011-04-27 . Retrieved 2009-07-20.a b c d "History: 1990s". SK Hynix. Archived from the original on 5 February 2021 . Retrieved 6 July 2019.
Burger D, Goodman J, Kägi A (1996) Memory bandwidth limitations of future microprocessors. In: Proceedings of the 23rd International Symposium on Computer Architecture, Philadelphia, 22–24 May 1996. IEEE/ACM, Los Alamitos/New York, pp 78–89Imani M, Gupta S, Rosing T. Ultra-efficient processing in-memory for data intensive applications. In: Proceedings of the 54th ACM/EDAC/IEEE Design Automation Conference (DAC), Austin, 2017. 1–6 Just like with training ML models, knowing what regime you're in allows you to narrow in on optimizations that matters. For example, if you're spending all of your time doing memory transfers (i.e. you are in a memory-bandwidth bound regime), then increasing the FLOPS of your GPU won't help. On the other hand, if you're spending all of your time performing big chonky matmuls (i.e. a compute-bound regime), then rewriting your model logic into C++ to reduce overhead won't help.