  Machine learning at the edge offers great benefits such as increased privacy
and security, low latency, and more autonomy. However, a major challenge is
that many devices, in particular edge devices, have very limited memory, weak
processors, and scarce energy supply. We propose a hybrid hardware-software
framework that has the potential to significantly reduce the computational
complexity and memory requirements of on-device machine learning. In the first
step, inspired by compressive sensing, data is collected in compressed form
simultaneously with the sensing process. Thus this compression happens already
at the hardware level during data acquisition. But unlike in compressive
sensing, this compression is achieved via a projection operator that is
specifically tailored to the desired machine learning task. The second step
consists of a specially designed and trained deep network. As concrete example
we consider the task of image classification, although the proposed framework
is more widely applicable. An additional benefit of our approach is that it can
be easily combined with existing on-device techniques. Numerical simulations
illustrate the viability of our method.
