Memory Wall

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Gholami A, Kim S, Yao Z, Dong Z, Mahoney M, Keutzer K, A Survey of Quantization Methods for Efficient Neural Network Inference, arxiv preprint, arxiv:arXiv:2103.13630, 2021. To continue the innovations and break the memory wall, we need to rethink the design of AI models. There are several issues here. First, the current methods for designing AI models are mostly ad-hoc, and/or involve very simple scaling rules. For instance, recent large Transformer models are mostly just a scaled version of almost the same base architecture proposed in the original BERT model [22]. Second, we need to design more data efficient methods for training AI models. Current NNs require a huge amount of training data and hundreds of thousands of iterations to learn, which is very inefficient. Some might note that it is also different from how human brains learn, which often only require very few examples per concept/class. Third, the current optimization and training methods need a lot of hyperparameter tuning (such as learning rate, momentum, etc.), which often results in hundreds of trial and error sweeps to find the right setting to train a model successfully. As such, the training cost reported in Figure 1 is only a lower bound of the actual overhead, and the true cost is typically much higher. Fourth, the prohibitive size of the SOTA NN models makes their deployment for inference very challenging. This is not just restricted to models such as GPT-3. In fact, deploying large recommendation systems (which are similar to Transformers but which have much larger embedding and very few MLP layers afterwards [23]) that are used by hyperscalar companies is a major challenge. Finally, the design of hardware accelerators has been mainly focused on increasing peak compute with relatively less attention on improving memory-bound workloads. This has made it difficult both to train large models, as well as to explore alternative models, such as Graph NNs which are often bandwidth-bound and cannot efficiently utilize current accelerators. The invention of the MOSFET (metal–oxide–semiconductor field-effect transistor), also known as the MOS transistor, by Mohamed M. Atalla and Dawon Kahng at Bell Labs in 1959, [11] led to the development of metal–oxide–semiconductor (MOS) memory by John Schmidt at Fairchild Semiconductor in 1964. [9] [12] In addition to higher speeds, MOS semiconductor memory was cheaper and consumed less power than magnetic core memory. [9] The development of silicon-gate MOS integrated circuit (MOS IC) technology by Federico Faggin at Fairchild in 1968 enabled the production of MOS memory chips. [13] MOS memory overtook magnetic core memory as the dominant memory technology in the early 1970s. [9] One of the Most Successful 16K Dynamic RAMs: The 4116". National Museum of American History. Smithsonian Institution . Retrieved 20 June 2019. Takeuchi, Kei (1998). "16M-BIT SYNCHRONOUS GRAPHICS RAM: μPD4811650". NEC Device Technology International (48) . Retrieved 10 July 2019.

Williams, F. C.; Kilburn, T.; Tootill, G. C. (Feb 1951), "Universal High-Speed Digital Computers: A Small-Scale Experimental Machine", Proc. IEE, 98 (61): 13–28, doi: 10.1049/pi-2.1951.0004, archived from the original on 2013-11-17. Both static and dynamic RAM are considered volatile, as their state is lost or reset when power is removed from the system. By contrast, read-only memory (ROM) stores data by permanently enabling or disabling selected transistors, such that the memory cannot be altered. Writeable variants of ROM (such as EEPROM and NOR flash) share properties of both ROM and RAM, enabling data to persist without power and to be updated without requiring special equipment. ECC memory (which can be either SRAM or DRAM) includes special circuitry to detect and/or correct random faults (memory errors) in the stored data, using parity bits or error correction codes. Boroumand A, Zheng H, Mutlu O, et al. CoNDA: efficient cache coherence support for near-data accelerators. In: Proceedings of ACM/IEEE 46th Annual International Symposium on Computer Architecture (ISCA), Phoenix, 2019. 629–642Xu S, Chen X, Wang Y, et al. PIMSim: a flexible and detailed processing-in-memory simulator. IEEE Comput Arch Lett, 2019, 18: 6–9 a b Sah, Chih-Tang (October 1988). "Evolution of the MOS transistor-from conception to VLSI" (PDF). Proceedings of the IEEE. 76 (10): 1280–1326 (1303). Bibcode: 1988IEEEP..76.1280S. doi: 10.1109/5.16328. ISSN 0018-9219.

Singh G, Gomez-Luna J, Mariani G, et al. NAPEL: near-memory computing application performance prediction via ensemble learning. In: Proceedings of the 56th ACM/IEEE Design Automation Conference (DAC), Las Vegas, 2019. 1–6 Eager mode can be thought of as a standard scripting execution method. The deep learning framework executes each operation immediately, as it is called, line by line, like any other piece of Python code. This makes debugging and understanding your code more accessible, as you can see the results of intermediate operations and see how your model behaves.a b c "Electronic Design". Electronic Design. Hayden Publishing Company. 41 (15–21). 1993. The first commercial synchronous DRAM, the Samsung 16-Mbit KM48SL2000, employs a single-bank architecture that lets system designers easily transition from asynchronous to synchronous systems. Wu B, Iandola F, Jin PH, Keutzer K. Squeezedet: Unified, small, low power fully convolutional neural networks for real-time object detection for autonomous driving. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops 2017 (pp. 129–137). 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. Hoefler T, Alistarh D, Ben-Nun T, Dryden N, Peste A. Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks. arXiv preprint arXiv:2102.00554. 2021 Jan 31. michaeljordan on 8th of May – Summary of Edgar Allen Poe ‘The Masque of the Red Death’ discussion by Sophie Smars

Singh G, Chelini L, Corda S, et al. Near-memory computing: past, present, and future. Microprocessors Microsyst, 2019, 71: 102868 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 78–89 Professors Tajana Rosing and Rob Knight, both faculty in the Department of Computer Science and Engineering at the University of California, San Diego, took the lead as primary investigators on the new CRISP initiative in addition to continuing their work contributing to faster cancer treatments. Knight is also a founding director of the Center for Microbiome Innovation in the Jacobs School of Engineering at UC San Diego and his research lab is a part of the UC San Diego School of Medicine.Another important solution is to design optimization algorithms that are robust to low precision training. In fact, one of the major breakthroughs in AI accelerators has been the use of half-precision (FP16) arithmetic, instead of single precision [5,6]. This has enabled more than 10x increase in hardware compute capability. However, it has been challenging to further reduce the precision from half-precision to INT8 without accuracy degradation with current optimization methods. Summarize the experiences and ask the players to take a moment to silently recognize and appreciate those who have contributed to their work life in a positive way. Follow this game up with Happy Hour! What a wonderful man you were. A treasured father, grandfather and great grandfather who will be so incredibly and dearly missed. We still can't believe that you are no longer here with us bringing smiles to our faces every day as you always did. You will forever live on in our hearts through the wonderful memories we have shared and captured together. We hope you can now rest without suffering, reunited with Eleanor and Eileen. Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size. arXiv preprint arXiv:1602.07360. 2016 Feb 24.

Google’s TensorFlow/Jax and other graph mode execution pipelines generally require the user to ensure their model fits into the compiler architecture so that the graph can be captured. Dynamo changes this by enabling partial graph capture, guarded graph capture, and just-in-time recapture. 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 Samsung Electronics Develops Industry's First Ultra-Fast GDDR4 Graphics DRAM". Samsung Semiconductor. Samsung. October 26, 2005 . Retrieved 8 July 2019. In SRAM, the memory cell is a type of flip-flop circuit, usually implemented using FETs. This means that SRAM requires very low power when not being accessed, but it is expensive and has low storage density.This two-phase approach makes it more challenging to understand and debug your code, as you cannot see what is happening until the end of the graph execution. This is analogous to "interpreted" vs. "compiled" languages, like python vs. C++. It's easier to debug Python, largely since it's interpreted. Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861. 2017 Apr 17. Chris Jesshope and Colin Egan (2006). Advances in Computer Systems Architecture: 11th Asia-Pacific Conference, ACSAC 2006, Shanghai, China, September 6-8, 2006, Proceedings. Springer. p.109. ISBN 9783540400561. Archived from the original on August 1, 2016 . Retrieved March 31, 2014. Seshadri V, Lee D, Mullins T, et al. Ambit: in-memory accelerator for bulk bitwise operations using commodity DRAM technology. In: Proceedings of the 50th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), Boston, 2017. 273–287 Samsung Begins Producing The Fastest GDDR6 Memory In The World". Wccftech. 18 January 2018 . Retrieved 16 July 2019.