Aspinity’s analogML™ (analog machine learning) core combines the sophisticated functionality of a tinyML chip with low-power analog neuromorphic computing concepts to deliver a revolutionary system-level approach to low-power edge processing. Built on the RAMP™ technology platform, the analogML core is a fully analog inferencing solution that classifies raw, unstructured sensor data in the analog domain, without the need for power-hungry digitization and digital processors.By more quickly eliminating the irrelevant data at the very beginning of the signal chain, an analogML core extends battery life by 10x or more for always-on edge processing applications such as:
- Voice activity detection for smart home, consumer, and IoT devices
- Glass break, alarm, or other acoustic event detection for home monitoring
- Vibration monitoring for predictive maintenance
- Heart rate anomoly detection in biomedical devices
In a traditional always-on edge system, data relevance can only be determined after digitization. Since system power consumption is dominated by the ADC and digital processors, a digitize-first architecture is grossly inefficient and wastes significant power analyzing data that will simply be thrown away.
An analogML core eliminates this inefficiency by bringing near-zero-power inferencing into the analog domain in an analyze-first architecture. Data relevance is determined prior to digitization, allowing the higher-power digital system to remain off unless important data are detected.
- Sensor interfacing: Interface circuitry can be synthesized for specific sensor types (microphone, accelerometer, etc.)
- Analog feature extraction: Picks out salient features from raw, analog sensor data, drastically reduces the amount of data going into the neural network.
- Analog neural network: Efficient, small-footprint analog inferencing block programmed with machine learning models that are developed using standard training environments.
- Analog data compression: Continuous collection and compression of analog sensor data for low-power data buffering
Aspinity’s complete development environment allows engineers to build, compile, and load application specific analog machine learning models onto the analogML core. It has been specifically designed for engineers without analog expertise to be able to use standard training data and the standard programming interfaces that they are already accustomed to using.