Blueshift Memory in Cambridge is to develop a memory architecture for computer vision in embedded IoT applications.
The company has received a UK grant from Innovate UK for a 13 month project using its ‘Cambridge’ high speed memory architecture. This optimizes the memory architecture to the data structures on the applications for more efficient handling of large data sets and time-critical data, enabling up to 1,000 times faster memory access.
The project involves custom configuration of a Field Programmable Gate Array (FPGA) using deep learning, optimising it for faster performance and better power efficiency. It is also planning to integrate this technology into an Applications-Specific Integrated Circuit (ASIC), along with RISC-V processor capability.
Computer vision is crucial to solving a wide range of real-world problems in fields including robotics, Industry 4.0, Smart Cities and autonomous vehicles, but it currently requires a significant level of computing capacity that necessitates high power consumption.
“By dramatically increasing memory access speed, the compact CV AI module we are developing will open up use cases such as onboard real-time scenario analysis in body-worn cameras,” said Peter Marosan, Founder and CEO of Blueshift Memory.
“It is a great achievement for Blueshift Memory to have won a Smart Grant for this work, as it is a highly competitive selection procedure. Our project is one of only 71 out of a total of 1072 applications that were successful in securing funding in this round,” he added.
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