The emergence of Artificial Intelligence (AI) and Machine Learning (ML) is one of the most significant computing trends in recent history. According to research, by 2027, spending on AI software alone will grow to nearly $300B, with a CAGR of 19.1% [1]. And, as the software half of the AI/ML world is growing at meteoric rates, the hardware side is also teeming with innovation. Similar research suggests that the AI accelerator market will increase from $21B in 2024 to $33B by 2028 [2].
AI/ML is a software-driven pursuit and can have very different requirements depending on the market and application (i.e., automotive, IoT, cloud, training vs inference, etc). Supporting this fast moving and diverse software market requires a collaborative,industry-wide effort to develop flexible and customized domain-specific hardware solutions optimized for this range of AI workloads. Innovation means that the market should not be limited to a few key players. Rather, the process needs to be democratized to enable everyone, enterprises and startups, to contribute their ideas to the growing AI/ML computing landscape.
