Memory Wall

Product Information

He M, Song C, Kim I, et al. Newton: a DRAM-maker’s accelerator-in-memory (AiM) architecture for machine learning. In: Proceedings of the 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), Athens, 2020. 372–385 Memory (Bandwidth): Waiting for data or layer weights to get to the compute resources. Common examples of bandwidth-constrained operations are various normalizations , pointwise operations , SoftMax , and ReLU .

Ishii Y, Inaba M, Hiraki K. Access map pattern matching for high performance data cache prefetch. J Instruction-Level Parallelism, 2011, 13: 499–500

a b c d "History: 1990s". SK Hynix. Archived from the original on 5 February 2021 . Retrieved 6 July 2019.

As the meeting leader, first ask for volunteers to approach the wall and discuss memories they posted and want to share. When you’ve run out of volunteers, approach memories on the wall that catch your eye and ask for the owner to share the story. Liu C, Sivasubramaniam A, Kandemir M (2004) Organizing the last line of defense before hitting the memory wall for CMPs. In: Proceedings of the 10th IEEE Symposium on High Performance Computer Architecture, Madrid, 14–18 Feb 2004. IEEE, Los Alamitos, pp 176–185

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] Triton bridges the gap enabling higher-level languages to achieve performance comparable to those using lower-level languages. The Triton kernels themselves are quite legible to the typical ML researcher which is huge for usability. Triton automates memory coalescing, shared memory management, and scheduling within SMs. Triton is not particularly helpful for the element-wise matrix multiplies, which are already done very efficiently. Triton is incredibly useful for costly pointwise operations and reducing overhead from more complex operations such as Flash Attention that involve matrix multiplies as a portion of a larger fused operation. The two widely used forms of modern RAM are static RAM (SRAM) and dynamic RAM (DRAM). In SRAM, a bit of data is stored using the state of a six- transistor memory cell, typically using six MOSFETs. This form of RAM is more expensive to produce, but is generally faster and requires less dynamic power than DRAM. In modern computers, SRAM is often used as cache memory for the CPU. DRAM stores a bit of data using a transistor and capacitor pair (typically a MOSFET and MOS capacitor, respectively), [27] which together comprise a DRAM cell. The capacitor holds a high or low charge (1 or 0, respectively), and the transistor acts as a switch that lets the control circuitry on the chip read the capacitor's state of charge or change it. As this form of memory is less expensive to produce than static RAM, it is the predominant form of computer memory used in modern computers. 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. 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.

Chen W H, Li K X, Lin W Y, et al. A 65 nm 1 Mb nonvolatile computing-in-memory ReRAM macro with sub-16 ns multiply-and-accumulate for binary DNN AI edge processors. In: Proceedings of IEEE International Solid-State Circuits Conference, San Francisco, 2018. 494–496 Valavi H, Ramadge P J, Nestler E, et al. A 64-Tile 2.4-Mb in-memory-computing CNN accelerator employing charge-domain compute. IEEE J Solid-State Circ, 2019, 54: 1789–1799 The 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. Mittal S. A survey of ReRAM-based architectures for processing-in-memory and neural networks. Mach Learn Knowl Extr, 2018, 1: 75–114 Samsung Develops the Industry's Fastest DDR3 SRAM for High Performance EDP and Network Applications". Samsung Semiconductor. Samsung. 29 January 2003 . Retrieved 25 June 2019.Oliveira G F, Santos P C, Alves M A Z, et al. A generic processing in memory cycle accurate simulator under hybrid memory cube architecture. In: Proceedings of 2017 International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS), Pythagorion, 2017. 54–61 a b "Samsung Electronics Comes Out with Super-Fast 16M DDR SGRAMs". Samsung Electronics. Samsung. 17 September 1998 . Retrieved 23 June 2019. In the final year of the grant, the center’s investigative teams will continue testing their new architectures in three primary areas of application: targeted cancer treatments, analytics for large datasets and video analysis. As a common example, the BIOS in typical personal computers often has an option called "use shadow BIOS" or similar. When enabled, functions that rely on data from the BIOS's ROM instead use DRAM locations (most can also toggle shadowing of video card ROM or other ROM sections). Depending on the system, this may not result in increased performance, and may cause incompatibilities. For example, some hardware may be inaccessible to the operating system if shadow RAM is used. On some systems the benefit may be hypothetical because the BIOS is not used after booting in favor of direct hardware access. Free memory is reduced by the size of the shadowed ROMs. [28] Recent developments It is important to note that the memory requirements to train AI models are typically several times larger than the number of parameters. This is because training requires storing intermediate activations, and this typically adds 3–4x more memory than the number of parameters (excluding embeddings). This is illustrated in Figure 3, where the total training memory footprint is shown for training different flagship AI models throughout the years. We can clearly see how the design of SOTA Neural Network (NN) models has been implicitly influenced by the DRAM capacity of the accelerators in different years.

The center also has funded 185 graduate students across the participating universities, 59 of whom have graduated and gone on to jobs in important sectors such as the U.S. semiconductor industry and as faculty in U.S. universities. Skadron said the center’s work has also provided opportunities for undergraduate researchers at UVA and supported innovations in curriculum for computer systems design.Xu S, Chen X, Han Y, et al. TUPIM: a transparent and universal processing-in-memory architecture for unmodified binaries. In: Proceedings of the 2020 on Great Lakes Symposium on VLSI (GLSVLSI’20). New York: Association for Computing Machinery, 2020. 199–204 We are committed to improving the care and quality of life of our patients and their families through ongoing research, innovation, service transformation and improvement. Find out more a b "Samsung Electronics Announces JEDEC-Compliant 256Mb GDDR2 for 3D Graphics". Samsung Electronics. Samsung. 28 August 2003 . Retrieved 26 June 2019. Fine CMOS techniques create 1M VSRAM". Japanese Technical Abstracts. University Microfilms. 2 (3–4): 161. 1987.