(Sitao Huang, Vikram Sharma
Mailthody, Zaid Qureshi- Presenting Wed. 2/6 at 2PM ET)
The increasing
deployment of machine learning for applications such as image analytics and
search has resulted in a number of special purpose accelerators in this domain.
In this talk, we present two of our recent works. The first work is a compiler
that optimizes the mapping of the computation graph for efficient execution in
a memristor-based hybrid (analog-digital) deep learning accelerator. By
building the compiler, we have made special
purpose accelerators more accessible to software developers,
and enabled the generation of better-performing executables. In our second work ,
we are exploring near-memory accelerations for applications like image
retrieval. These applications are constrained by the bandwidth available to
accelerators like GPUs, which could potentially benefit from near-memory
processing.
