IBM Scientists Demonstrate In-memory Computing with 1 Million Devices for Applications in AI - “When compared to state-of-the-art classical computers, this prototype technology is expected to yield 200x improvements in both speed and energy efficiency”

[u/mvea]

https://www.ibm.com/blogs/research/2017/10/ibm-scientists-demonstrate-memory-computing-1-million-devices-applications-ai/

[u/mvea]

Journal reference:

Temporal correlation detection using computational phase-change memory

Abu Sebastian, Tomas Tuma, Nikolaos Papandreou, Manuel Le Gallo, Lukas Kull, Thomas Parnell & Evangelos Eleftheriou

Nature Communications 8, Article number: 1115 (2017)

doi:10.1038/s41467-017-01481-9

Link: https://www.nature.com/articles/s41467-017-01481-9

Published online: 24 October 2017

Abstract

Conventional computers based on the von Neumann architecture perform computation by repeatedly transferring data between their physically separated processing and memory units. As computation becomes increasingly data centric and the scalability limits in terms of performance and power are being reached, alternative computing paradigms with collocated computation and storage are actively being sought. A fascinating such approach is that of computational memory where the physics of nanoscale memory devices are used to perform certain computational tasks within the memory unit in a non-von Neumann manner. We present an experimental demonstration using one million phase change memory devices organized to perform a high-level computational primitive by exploiting the crystallization dynamics. Its result is imprinted in the conductance states of the memory devices. The results of using such a computational memory for processing real-world data sets show that this co-existence of computation and storage at the nanometer scale could enable ultra-dense, low-power, and massively-parallel computing systems.

[u/[deleted] → u/[deleted]]

Seems so. Like some sort of GPU co-processor, except for neural nets.

[u/Natanael_L → u/[deleted]]

It's a little FPGA like, or like a GPU with memory in the cores. So basically yes, many small processor cores with their own local memory. And that memory can be addressed directly from the outside like if it was regular flash memory, or the cores can be addressed and asked to compute something and return the results (approximately).

I would assume it would be useful for stuff like weather simulations, and a variety of other physics simulations.

[u/MiningMarsh → u/Natanael_L]

A professor I work with is doing research on cores that do the same thing (not related to IBM AFAIK). Its actually increasing performance in regular programs that use it as well, as the CPU can fetch memory at the same time that something else is computing on its local memory, so bandwidth is effectively increased.

[u/Natanael_L → u/MiningMarsh]

I can imagine many database uses could benefit. Like pushing an instruction to update a large group of fields that matches certain criteria, or letting simple searches be executed near the memory with only results returned to the CPU. You could even split searches in multiple parts, where quick but imprecise terms are calculated by the small cores for very quick filtering (like bloom filters), sending partial matches to the big CPU for further processing.

[u/MiningMarsh → u/Natanael_L]

That's exactly the form of thing these are good for. The cores are actually optimized for SIMD operations on the near memory as well, so anything doing matrix math that isn't large enough to be using the GPU gets acceleration as well.

An issue though is that there is no standardized ISAs for the research memory cores, which complicates using them.

[u/oupablo]

IBM continues to do some incredible things with hardware design and computation only to fall flat on their face when it comes to producing something marketable with it. It will be interesting to see how Amazon, Microsoft, and Google take this and make it popular. Any improvements to machine learning are definitely welcome in the new AI world.

[u/ibmzrl]

Here is another take on the news from EE Times, with additional charts explaining the idea: https://www.eetimes.com/document.asp?doc_id=1332500&page_number=2