The 2nd generation Akida neural processors feature a unique neuromorphic approach to bring hyper-efficient acceleration for AI models at the edge.
BrainChip’s second-generation Akida platform is a powerful neural processing system, architected for embedded Edge AI applications. The Akida neural processors features a unique neuromorphic approach to bring hyper-efficient acceleration for AI models at the edge. The Akida platform offers simpler implementations by consuming raw data directly from sensors which drastically reduces model size and operations performed while maintaining very high accuracy. Akida platform enables edge learning local to the chip, independent of the cloud, dramatically reducing latency while improving privacy and data security. This can enable design engineers to shrink design cycles and lower the cost of development while improving the security of the device.
The 2nd Gen Akida platform offers efficient 8-bit processing suitable for time domain convolutions and vision transformer acceleration, for high performance in sub-watt devices. Advancements in AI require parallel advances in on-device learning capabilities while simultaneously overcoming the challenges of efficiency, scalability, and latency. The on-chip AI offers benefits in terms of performance and cost. It also includes temporal event-based neural nets (TENN) spatial-temporal convolutions that supercharge the processing of raw time-continuous streaming data, such as video analytics, target tracking, audio classification, analysis of MRI and CT scans for vital signs prediction, and time series analytics used in forecasting, and predictive maintenance.
“We see an increasing demand for real-time, on-device, intelligence in AI applications powered by our MCUs and the need to make sensors smarter for industrial and IoT devices,” said Roger Wendelken, Senior Vice President in Renesas’ IoT and Infrastructure Business Unit. “We licensed Akida neural processors because of their unique neuromorphic approach to bring hyper-efficient acceleration for today’s mainstream AI models at the edge. With the addition of advanced temporal convolution and vision transformers, we can see how low-power MCUs can revolutionize vision, perception, and predictive applications in a wide variety of markets like industrial and consumer IoT and personalized healthcare, just to name a few.”
The Akitda platform has been developed by BrainChip, a company which develops edge AI on-chip processing and learning. Akida uses neuromorphic principles to mimic the human brain, analyzing only essential sensor inputs at the point of acquisition, and processing data with efficiency, precision, and economy of energy. The 2nd Generation of Akida can enhance vision, perception, and predictive applications in markets such as industrial and consumer IoT and personalized healthcare, etc.
