AnalogML™ Core

Your solution for system level power efficiency at the edge

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:

 


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.
The analogML core consists of multiple software-controlled analog processing blocks that can be enabled, reconfigured, and tuned for various analyze-first applications such as smart home, IoT, consumer, industrial, and biomedical applications.  Since the analogML core is a fully analog processing chip, there is no clock and  each of the processing blocks can be powered independently when needed. These blocks are arranged as shown in the figure above and provide the following functionality:
  • 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.

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