The memBrain™ neuromorphic memory by Silicon Storage Technology is tailor-made for edge-based AI operations, ensuring efficient handling of deep neural network workloads. By transitioning AI processing from the cloud closer to the network edge, memBrain™ addresses the limitations faced by battery-powered and deeply embedded AI devices. It utilizes SuperFlash® technology with specific optimizations aimed at enhancing Vector Matrix Multiplication (VMM), a core requirement in neural network inference processing.\n\n MemBrain™ significantly improves data processing by utilizing an analog compute-in-memory method. It efficiently manages the storage and retrieval of synaptic weights the neural nets require for inference operations. By storing weights within the floating gate structure, memBrain™ offers remarkable reductions in system latency, often surpassing traditional digital processor approaches. The solution practically reduces the dependency on off-chip memory fetches, which typically bottleneck performance.\n\n The memBrain™ architecture allows for vast improvements in power savings and cost efficiency. Designed to outperform traditional DSP and DRAM/SRAM solutions, it offers 10 to 20 times reduction in power consumption. Additionally, the innovative tile-based multiplication and summation characteristics support extensive neural network operations, bolstering its application in the realm of edge-based, low-power AI devices.
