Stand-Alone Edge Computing with GAP8
Stand-Alone Edge Computing with GAP8 (status:completed)
Current trends in low power systems point towards an edge computing paradigm. As such, sensing systems can have significant computational resources to analyze and aggregate data before forwarding data to the cloud. This can be particularly challenging with data intensive sensors such as cameras or microphones. In recent years, machine learning has become the most effective way to extract the most meaningful information from these data sources. However, until very recently, running machine learning on low-power, resource constrained devices was very inefficient in terms of memory, and energy.
IoT application processors like GAP8 are a key building block for integrating artificial intelligence and advanced classification into next-generation wireless sensing devices. Thanks to its 8 RISC-V cores and its convolution hardware accelerator, GAP8 can perform complex computation with a mW-range power budget. In our lab, we have developed a fully-integrated GAP8-based sensor node with video and audio processing capabilities, as well as low power, long range communication.
In this project, the student will continue developing this sensor node by developing an application to classify audio segments. There is already an existing audio classifier implemented using an XNOR network on GAP8. This application can be used as a basis to have a functioning stand-alone device that also uses communication. In addition to exploiting parallelism, the student can also implement ISA-level (e.g. using intrisics or HWCE for the non-binary layers) improvements or develop energy-aware mapping algorithms to improve the energy efficiency of the application.
This project will be developed in close collaboration with MiroMico AG (Zurich).
(not all need to be met by the single candidate)
Experience using the laboratory instrumentation - signal generators, oscilloscopes, DAQ cards, Matlab etc.. knowledge of microcontroller programming and PC programming (C/C++, preferably embedded C) basic knowledge or interests on signal processing, wireless communication and machine learning Motivation to build and test a real system
Detailed Task Description
A detailed task description will be worked out right before the project, taking the student's interests and capabilities into account.
Renzo Andri, email@example.com
Andres Gomez, firstname.lastname@example.org