As artificial intelligence (AI) processing moves from the cloud to the edge of the network, battery powered and deeply embedded devices are challenged to perform AI functions – like computer vision and voice recognition. Microchip Technology, via its Silicon Storage Technology (SST) subsidiary, is addressing this challenge by significantly reducing power with its analog memory technology, the memBrain neuromorphic memory solution.
Based on its industry proven SuperFlash technology and optimised to perform vector matrix multiplication (VMM) for neural networks, Microchip’s analog Flash memory solution improves system architecture implementation of VMM through an analog in-memory compute approach, enhancing AI inference at the edge.
As current neural net models may require 50 million or more synapses (weights) for processing, it becomes challenging to have enough bandwidth for an off-chip DRAM, creating a bottleneck for neural net computing and an increase in overall compute power. In contrast, the memBrain solution stores synaptic weights in the on-chip floating gate – offering significant improvements in system latency. When compared to traditional digital DSP and SRAM/DRAM based approaches, it delivers 10 to 20 times lower power and significantly reduced overall BOM.
The memBrain solution is being adopted by today’s companies looking to advance machine learning capacities in edge devices. Due to its ability to significantly reduce power, this analog in-memory compute solution is ideal for any AI application.
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