  Low-level sensory data processing in many Internet-of-Things (IoT) devices
pursue energy efficiency by utilizing sleep modes or slowing the clocking to
the minimum. To curb the share of stand-by power dissipation in those designs,
near-threshold/sub-threshold operational points or ultra-low-leakage processes
in fabrication are employed. Those limit the clocking rates significantly,
reducing the computing throughputs of individual processing cores. In this
contribution we explore compensating for the performance loss of operating in
near-threshold region (Vdd =0.6V) through massive parallelization. Benefits of
near-threshold operation and massive parallelism are optimum energy consumption
per instruction operation and minimized memory roundtrips, respectively. The
Processing Elements (PE) of the design are based on Transport Triggered
Architecture. The fine grained programmable parallel solution allows for fast
and efficient computation of learnable low-level features (e.g. local binary
descriptors and convolutions). Other operations, including Max-pooling have
also been implemented. The programmable design achieves excellent energy
efficiency for Local Binary Patterns computations.
