The rapid advance of artificial intelligence (AI) is impacting everything from how we drive to how we make business decisions and shop. Enabled by the massive and growing volume of big data, AI is also causing compute demand to balloon. In fact, the most recent generative AI models require a 10 to 100-fold increase in computing power to train models compared to the previous generation, which is, in turn, doubling overall demand about every six months.
As you might expect, this has led to a computing transformation that has, in part, been made possible due to new types of memory architectures. These advanced graphics processing unit (GPU) architectures are opening up dramatic new possibilities for designers. The key is choosing the right memory architecture for the task at hand and the right memory to deploy for that architecture.
To be sure, there is an array of more efficient emerging memories out there for specific tasks. They include compute-in-memory SRAM (CIM), STT-MRAM, SOT-MRAM, ReRAM, CB-RAM, and PCM. While each has different properties, as a collective unit they serve to enhance compute power while raising energy efficiency and reducing cost. These are key factors that must be considered to develop economical and sustainable AI SoCs.
Many considerations affect a designer’s choice of architecture according to the priorities of any given application. These include throughput, modularity and scalability, thermal management, speed, reliability, processing compatibility with CMOS, power delivery, cost, and the need for analog behavior that mimics human neurons.
Let’s examine the features of the assorted emerging memories currently at a designer’s disposal.
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