www.eetimes.com, Aug. 27, 2024 –
Despite seven decades of mostly unsuccessful investigation, AI has experienced significant growth over the last 10 years, expanding at an exponential rate. This escalating adoption has been propelled by a shift toward highly parallel computing architectures, a departure from conventional CPU-based systems. Traditional CPUs, with their sequential processing nature that handles one instruction at a time, are increasingly unable to meet the demands of advanced, highly parallel AI algorithms.
A case in point: LLMs. This challenge has driven the widespread development of AI accelerators—specialized hardware engineered to dramatically enhance the performance of AI applications.
AI applications involve complex algorithms that include billions to trillions of parameters and require integer and floating-point multidimensional matrix mathematics at mixed precision ranging from 4-bits to 64-bits. Although the underlying mathematics consists of simple multipliers and adders, they are replicated millions of times in AI applications, posing a sizable challenge for computing engines.
AI accelerators come in various forms including GPUs, FPGAs and custom-designed application specific integrated circuits (ASICs). They offer dramatic performance enhancements over CPUs that result in faster execution times, as well as more efficient model deployment and scalability to handle increasingly complex AI applications.
The AI accelerator market is booming thanks to the widespread adoption of AI across a variety of industries. From facial/image recognition and natural language processing to self-driving vehicles and generative AI (GenAI) elaboration, AI is transforming how we live and work. This revolution has spurred a massive demand for faster, more efficient AI processing, making AI accelerators a crucial component of the AI infrastructure.
Notwithstanding the tremendous market growth, all existing commercial AI processing products have limitations, some more significant than others. AI processing can occur in two primary locations: in the cloud (data centers) or at the edge, each with distinct requirements and challenges.