The AI market size is projected to reach $1T this decade. Deep neural networks are widely deployed throughout this market, performing machine learning (or deep learning) tasks such as object recognition, natural conversation, weather forecasting, and video analysis.
About 1/3 of the AI market consists of intelligent sensors and smart devices at the internet of things' endpoints, including smartphones, laptops, drones and robots. This market is referred to as edge AI. Many deep learning systems on the edge are implemented directly on computing chips, to accelerate processing speed and reduce power consumption compared to software implementations.
The company has developed a new type of deep neural networks, which are particularly suitable for implementation on a chip. The underlying algorithms facilitate strong parallelization and include operations that can be done much faster in hardware, potentially leading to significant advantages over existing technology in many contexts.
I am raising 200K to build a prototype of this technology on a field-programmable gate array (FPGA) and demonstrate its performance. Based on a successful prototype, the company will proceed to produce deep learning chips embedding its technology for appropriate edge AI use cases.
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