Ousterhout J (1990) Why aren’t operating systems getting faster as fast as hardware? In: Proceedings of the Summer USENIX Technical Conference, June 1990, pp 247–256 The Cutting Edge of IC Technology: The First 294,912-Bit (288K) Dynamic RAM". National Museum of American History. Smithsonian Institution . Retrieved 20 June 2019. An integrated bipolar static random-access memory (SRAM) was invented by Robert H. Norman at Fairchild Semiconductor in 1963. [14] It was followed by the development of MOS SRAM by John Schmidt at Fairchild in 1964. [9] SRAM became an alternative to magnetic-core memory, but required six MOS transistors for each bit of data. [15] Commercial use of SRAM began in 1965, when IBM introduced the SP95 memory chip for the System/360 Model 95. [10] Kevin Skadron, Harry Douglas Forsyth Professor of Computer Science, heads up the UVA-led, nine-university research center that is creating the ultra-fast computing systems that pair data processing and memory in a single unit. 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.

Elpida ships 2GB DDR2 modules". The Inquirer. 4 November 2003. Archived from the original on July 10, 2019 . Retrieved 25 June 2019. {{ cite news}}: CS1 maint: unfit URL ( link) 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. Yoshimoto, M.; Anami, K.; Shinohara, H.; Yoshihara, T.; Takagi, H.; Nagao, S.; Kayano, S.; Nakano, T. (1983). "A 64Kb full CMOS RAM with divided word line structure". 1983 IEEE International Solid-State Circuits Conference. Digest of Technical Papers. Vol.XXVI. pp.58–59. doi: 10.1109/ISSCC.1983.1156503. S2CID 34837669. 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. Samsung Electronics Develops First 128Mb SDRAM with DDR/SDR Manufacturing Option". Samsung Electronics. Samsung. 10 February 1999 . Retrieved 23 June 2019.In general, the term RAM refers solely to solid-state memory devices (either DRAM or SRAM), and more specifically the main memory in most computers. In optical storage, the term DVD-RAM is somewhat of a misnomer since, unlike CD-RW or DVD-RW, it does not need to be erased before reuse. Nevertheless, a DVD-RAM behaves much like a hard disc drive if somewhat slower. The first approach is quantization, a method that can be applied at the training and/or inference steps. While it has been very challenging to reduce the training precision much below FP16, it is possible to use ultra-low precision for inference. With current methods, it is relatively easy to quantize inference down to INT4 precision, with minimal impact on accuracy. This results in up to 8x reduction in model footprint and latency [7,8,19,20]. However, inference with sub-INT4 precision is more challenging and is currently a very active area of research. A second type, DRAM, is based around a capacitor. Charging and discharging this capacitor can store a "1" or a "0" in the cell. However, the charge in this capacitor slowly leaks away, and must be refreshed periodically. Because of this refresh process, DRAM uses more power, but it can achieve greater storage densities and lower unit costs compared to SRAM.

Jeddeloh J, Keeth B. Hybrid memory cube new DRAM architecture increases density and performance. In: Proceedings of Symposium on VLSI Technology (VLSIT), 2012

More hardware will be supported going forward, but the key is that Inductor dramatically reduces the amount of work a compiler team must do when making a compiler for their AI hardware accelerator. Furthermore, the code is more optimized for performance. There are significant reductions in memory bandwidth and capacity requirements. Four years into the five-year grant, the UVA-led, nine-university Center for Research in Intelligent Storage and Processing in Memory, or CRISP, has made strides that match the gargantuan problem the center is trying to solve. Boroumand A, Ghose S, Patel M, et al. LazyPIM: an efficient cache coherence mechanism for processing-in-memory. IEEE Comput Arch Lett, 2017, 16: 46–50 But processing just one sample would take weeks with today’s computers. Faster results were needed to get ahead of the virus’ spread and inform tactics for halting it. This is exactly where the center researchers’ hard work would prove invaluable.