SST's memBrain™ is a revolutionary neuromorphic memory product optimized for vector matrix multiplication (VMM) critical to neural network inference, offering significant advancements for AI applications at the edge. By leveraging an analog compute-in-memory approach, memBrain enhances system architecture, optimizing multiply-accumulate (MAC) operations crucial for deep neural networks (DNNs). This enables battery-powered devices to execute AI functions like voice and video recognition efficiently.\n\nOne of the standout features of memBrain is its ability to store synaptic weights directly in the floating gate, which minimizes system latency by reducing reliance on off-chip DRAM. This streamlined process leads to 10 to 20 times power reduction compared to traditional DSP and SRAM/DRAM methods, substantially improving inference frame latency and system cost. The memBrain system utilizes multiple interconnected "tiles," enabling support for large neural networks with rapid frame cycle times and low energy per MAC operation.\n\nThe architecture of memBrain supports scalability for varied AI applications, maintaining exceptional power efficiency and speed. It integrates seamless multiplication and summation operations within the memory array, allowing a compact design footprint (0.48 mm² per tile). As neural network models continue to expand, memBrain is positioned as a future-proof solution for AI and machine learning applications, providing a versatile and cost-effective alternative for developers aiming to push the boundaries of edge computing.
