Hailo is sampling its first of its deep learning processors, Hailo-8 chips. The new chip promises up to 26 tera operations per second (TOPS), and the company is now testing it with a number of select customers, mostly in the automotive industry.
Hailo made headlines when it raised a $12.5 million Series A round. At the time, the company was still at the hold-up for the first samples of its chips. Halio now says that its chips will outperform all other edge processors and do so at a smaller size and with fewer memory requirements.
“By designing an architecture that relies on the core properties of neural networks, edge devices can now run deep learning applications at full scale more efficiently, effectively, and sustainably than traditional solutions, while significantly lowering costs,” the company explains.
The company also argues that its chip outperforms Nvidia’s comparable Javier Xavier AGX in some benchmarks, all while using less power and hence running cooler, features that are important in small IoT devices.
It is yet to be seen if that works out in practice once more engineers utilize these chips, of course, but there can be no doubt that the demand for AI chips on the edge continues to increase.
In addition, Hailo is working with OEMs and tier-1 suppliers in the automotive industry to bring its chip to market, but it’s also looking at other verticals, including smart home products and other industry domains where a high-performance AI chip is needed for object detection and segmentation, for example.
“In recent years, we’ve witnessed an ever-growing list of applications unlocked by deep learning, which were made possible thanks to server-class GPUs,” said Orr Danon, CEO of Hailo. “However, as industries are increasingly powered and even upended by AI, there is a crucial need for an analogous architecture that replaces processors of the past, enabling deep learning to run devices at the edge. Hailo’s chip was designed from the ground up to do just that.
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