The year edge AI took off

The year edge AI took off

Feature articles |
By Nick Flaherty

The year started with the launch of Grai Matter and its edge AI chip, as well as an analysis of the role of IP.

New chip architectures also emerged, optimised for edge AI. The open source RISC-V instruction set architecture saw various instruction extensions to boost performance for edge AI and so lower the power consumption. While new techniques such as spiking neural networks promised to reduce power consumption further, French AI scientist Yann LeCun at Facebook highlighted the vital role at the RISC-V will play.

Funders also recognised the growth, backing startups in the middle of the year to get designs to the next stage of development.

By July, details of one of the major chip designs emerged with Blaize having raised $65m. With design teams in the UK, US and India, the company was focussed on both hardware and software.

The edge devices also have to be part of the Internet of Things (IoT)

Next: Mainstream chips fight back for edge AI 

While new chip designs were springing up all over the market, well established technologies were also pushing forwards. Nvidia’s Jetson card was pushing into more embedded applications, while software from Mipsology converted frameworks for inference on FPGAs  

Ethics is also emerging as a key factor.

Despite the scepticism earlier in the year, neuromorphic chips also saw plenty of innovation and development.

By December, the RISC-V momentum in edge AI was continuing, with new implementations.  

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