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Bangalore Startup, AlphaICs New Accelerator Can Oust NVIDIA GPUs

A banglaore-established startup named AlphaICs will sample a 13W machine-learning accelerator for cars, robots and drones.

A banglaore-established startup named AlphaICs will sample a 13W machine-learning accelerator for cars, robots and drones.

Mooted to beat Nvidia GPUs in recognizing images – Visteon is considering using the chip in future automotive systems based on test results on an FPGA version of the device.

AlphaICs designed an instruction set architecture (ISA) optimized for deep learning, reinforcement learning and other machine learning tasks. The startup aims to produce a family of chips with 16-256 cores, roughly spanning 2W to 200W.

The FPGA version of RAP-E beat Nvidia’s Volta V100 on a detailed image-recognition test using videos and convolutional neural net algorithms created by Visteon and run by AlphaICs at its labs. RAP-E beat Nvidia in all metrics by margins ranging from 50-400%.

“We were more than pleasantly surprised to see how good the technology was…so we are on the verge of engaging them at a deeper level and considering incorporating the chip in our products” for autonomous driving and in-car infotainment, said Vijay Nadkarni, an AI and augmented reality specialist at Visteon that mainly uses Nvdia chips for deep learning.

AlphaICs is part of an emerging group of startups that aims to take a broader look at a wider class of machine learning algorithms and ways to speed them up.

Neural Processing Data

The startup was formed by Vinod Dham, a veteran of several x86 designs, along with technical and business co-founders based in India.

“We are on a quest to build a new type of compute engine…There has to be a better architecture for deep learning, reinforcement learning and new types of machine learning,” said Dham, who designed Pentium processors at Intel then formed processor startups NexGen and Silicon Spice, sold to AMD and Broadcom, respectively.

AlphaICs’ first product, the 13W RAP-E, does inference and some learning on devices at the network’s edge and should be in production late next year. A higher end RAP-C will be a 100W chip using high bandwidth memory for building large neural networking models in data centers and will be in an FPGA version by June.

So far, the 25-person startup based in Bangalore raised about $15 million, enough to tape out its RAP-E in a TSMC 16FF process. It aims to raise a Series B over the next nine months to fund work on a 7nm version of RAP-C.

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