The Akida Neuromorphic IP offers unsurpassed performance on a performance-per-watt basis. The flexible Neural Processing Cores (NPCs) which form the Akida Neuron Fabric can be configured to perform convolutions (CNP) and fully connected (FNP) layers. Weight bit-precision is programmable to optimize for throughput or accuracy, with the weights stored locally in embedded SRAM inside each NPC. Entire neural networks can be placed into the fabric, removing the need to swap weights in and out of DRAM, which reduces power and increases throughput. Optionally networks layers and weights can be loaded from DRAM to support larger networks or dynamically updated networks for multiple sensors or pipelining. Since spiking neural networks are event-based, there is inherent sparsity in the deeper layers, further reducing power consumption. CNNs converted to SNNs become event-based and benefit from this sparsity.

The Akida Neuron Fabric enables SNNs to be placed in either a parallelized manner for ultimate performance or space- optimized to reduce silicon utilization and power consumption. Users can also modify the clock frequency, further optimizing performance and power consumption.