DeepCube raises Series A funding

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DeepCube, a specialist in deep learning, has closed $7 million in Series A funding that will be used to advance, productise, and expand the footprint of its patented software-based inference accelerator in new markets.

The round, led by Canadian VC Awz Ventures with participation from Koch Disruptive Technologies (KDT) and Nima Capital, brings the total invested in DeepCube to $12 million. The funding will enable the company to also invest in additional research, commercialisation, and growth of the DeepCube team in its offices in Tel Aviv and the US.

DeepCube’s Series A funding follows the launch of its software-based inference accelerator: currently the only technology that allows for efficient deployment of AI models on intelligent edge devices. Until now, deep learning deployments have remained limited due to the size and speed of neural networks, and the need for specialised hardware. DeepCube’s proprietary framework can be deployed on top of any existing hardware (CPU, GPU, ASIC) in both datacentres and edge devices, enabling over 10x speed improvement and memory reduction while delivering deep learning to edge devices, which has previously been unattainable at scale.

“Deep learning has accelerated in recent years. However, the ability to deploy and scale deep learning on edge devices, with a light footprint and efficient memory and processing power, is a significant challenge that has hindered adoption,” said Yaron Ashkenazi, Founder and Managing Partner, Awz Ventures. “DeepCube’s technology has the power to unlock truly autonomous decision making in semiconductors, datacentres, and on edge devices, while improving speed and memory reductions. This is absolutely critical to the future of deep learning.”

Commenting on the funding round Dr. Eli David, Co-Founder, DeepCube, said, “We are grateful for our investors’ vote of confidence in DeepCube and for their belief in our vision. With the new funding, we can deliver on the promise of deep learning to customers in new markets – having an impact not only on their businesses, but also, on the deep learning industry at large – far beyond what’s previously been possible.”