This work present a novel in-filter computing framework that can be used for designing ultra-light acoustic classifiers for use in smart internet-of-things (IoTs).
The result of this integration is a template-based SVM whose memory and computational footprint (training and inference) is light enough to be implemented on an FPGA-based IoT platform.
The tinyRadar project proposes a novel tinyML-based single-chip radar solution for on-edge sensing, detection of human activity, and gesture recognition.
For demonstration, we have successfully explored and implemented several compelling use cases for AI-enabled tinyRada
RAMAN
RAMAN, a Re-configurable and spArse tinyML Accelerator for infereNce on edge.
A demonstration video of RAMAN on the Efinix Ti60 FPGA board for the keyword spotting task, where the user’s spoken keywords control the maze game, can be found here
ARYABHAT: Neuromorphic Trainable Analog Integrated Chip
ARYABHAT: ANALOG RECONFIGURABLE TECHNOLOGY AND BIAS-SCALABLE HARDWARE FOR AI TASKS
