Spiking neural networks (SNNs) are a form of neuromorphic computing that provides a power- and computational-efficient artificial-intelligence (AI) model implementation. In the video (view above), BrainChip’s Chief Marketing Officer, Steven Brightfield, highlights some of the chips and systems mentioned here, including the new AKD1500. The earlier AKD1000 is available as a chip and in a number of form factors, such as M.2 and a PCI Express (PCIe) card (Fig. 1).
The AKD1500 can deliver 800 effective GOPS using only 1 mW/GOPS. BrainChip’s software suite can convert AI models from platforms like TensorFlow and PyTorch, so they work with this SNN AI accelerator to provide a faster and more power-efficient hardware implementation. The AKD1500 has an SPI and a PCIe interface for host support.
The chip and M.2 versions are useful, but BrainChip also released the AkidaTag reference design (Fig. 3). The reference design is a combination of the company’s AKD1500 AI accelerator with Nordic Semiconductor’s nRF5340 system-on-chip. The latter provides host processing support and Bluetooth connectivity. A matching smartphone app provides wireless feedback and control.
The AkidaTag uses its SPI interfaces to support sensors and peripherals. It can also interface with external serial-memory chips. The AkidaTag and other BrainChip platforms work with the Edge Impulse and BrainChip metaTF software, which is supported by the Akida Model Library.
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