Computing-in-memory technology is poised to eliminate the massive data communications bottlenecks otherwise associated with performing artificial intelligence (AI) speech processing at the network’s edge, but requires an embedded memory solution that simultaneously performs neural network computation and stores weights.
Microchip Technology, via its Silicon Storage Technology (SST) subsidiary, announced that its SuperFlash memBrain neuromorphic memory solution has solved this problem with the WITINMEM neural processing SoC (system-on-chip), the first in volume production that enables sub-mA systems to reduce speech noise and recognise hundreds of command words, in real time and immediately after power-up.
Microchip has worked with WITINMEM to incorporate Microchip’s memBrain analog in-memory computing solution, based on SuperFlash technology, into WITINMEM’s ultra-low-power SoC. The SoC features computing-in-memory technology for neural networks processing including speech recognition, voiceprint recognition, deep speech noise reduction, scene detection and health status monitoring. WITINMEM, in turn, is working with multiple customers to bring products to market during 2022 based on this SoC.
Microchip’s memBrain neuromorphic memory product is optimised to perform vector matrix multiplication (VMM) for neural networks. It enables processors used in battery-powered and deeply embedded edge devices to deliver the highest possible AI inference performance per Watt. This is accomplished by both storing the neural model weights as values in the memory array and using the memory array as the neural compute element. The result is 10 to 20 times lower power consumption than alternative approaches, along with lower overall processor bill of materials (BOM) costs because external DRAM and NOR are not required.
Permanently storing neural models inside the memBrain solution’s processing element also supports instant-on functionality for real-time neural network processing. WITINMEM has leveraged SuperFlash technology’s floating gate cells’ non-volatility to power down its computing-in-memory macros during the idle state to further reduce leakage power in demanding IoT use cases.
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