DeepCube, a startup developing a platform that reduces the computational requirements of AI algorithms on existing hardware, today raised $7 million. A spokesperson told VentureBeat the funds will be put toward research, commercialization, and growth of the DeepCube team at its offices in Tel Aviv and New York.
Machine learning deployments have historically been constrained by the size and speed of algorithms and the need for costly hardware. In fact, a report from MIT found that machine learning might be approaching computational limits. A separate Synced study estimated that the University of Washington’s Grover fake news detection model cost $25,000 to train in about two weeks. OpenAI reportedly racked up a whopping $12 million to train its GPT-3 language model, and Google spent an estimated $6,912 training BERT, a bidirectional transformer model that redefined the state of the art for 11 natural language processing tasks.
DeepCube, which describes its solution as a “software-based inference accelerator,” was cofounded in 2017 by Yaron Eitan and Eli David, who previously founded AI cybersecurity company Deep Instinct. The two developed a platform that enables machine learning models to run efficiently on edge devices and servers. DeepCube’s product is designed to be deployed on any type of hardware, including processors, GPUs, and AI accelerators, and the company claims it leads to an average tenfold speed improvement, a “major” reduction in memory requirements, and a “substantial” reduction in power consumption.