OmniML is artificial intelligence (AI) company that aims to amplify and enable powerful machine learning capabilities to edge devices. OmniML enables greater speed, accuracy, and efficiency in AI by creating deep learning models that bridge the gap between AI applications and the diverse range of devices found on the edge. OmniML is backed by established VCs and world-leading researchers and industry experts. OmniML makes major ML tasks 10x faster on different edge devices with 1/10th of the engineering effort. OmniML’s technology has already demonstrated significant gains in model performance and cost reduction for many enterprise customers in multiple vertical markets. OmniML was founded in 2021 and is headquartered in San Jose, CA.

Founded byDr.Song Han MIT EECS Professor and serial entrepreneur, Dr. Di Wu, former Facebook engineer, and Dr. Huizi Mao, co-inventor of the “deep compression” technology coming out of Stanford, OmniML solves a fundamental mismatch between AI applications and edge hardware to make AI more accessible for everyone, not just data scientists and developers. The core product offering is a model design platform that automates model co-design, training, and deployments targeting GPUs, AI SoCs, and even tiny MCUs.

AI is already improving our lives in all imaginable areas, many of which require AI to run on edge devices for latency, cost, privacy, etc. However, in the AI industry nowadays, there is still isn’t a good solution to design efficient models targeting AI capability on the increasingly diverse edge hardware. As a result, it takes repeated manual design and training iterations for model deployment, which in turn demands an extraordinary level of resource and engineering time for AI to reach production.

The team at OmniML is among a small cadre of AI/ML experts who know how to miniaturize deep learning models without sacrificing accuracy. As the publications from our research