Transparent Near-Memory Computing with a Reconfigurable Processor

Data intensive applications like machine learning or big data
analysis have stressed the requirements on memory subsystems. They
involve computational kernels whose performance is not limited by the
algorithmic complexity, but by the large amount of data they need
to process. To counteract the growing gap between computing power
and memory bandwidth, near-memory processing techniques have been
addressed to improve the performance in such applications significantly.
In this paper, we leverage a general purpose processor extended with a
reconfigurable fabric to execute hardware-accelerated instructions. This
fabric features a high-bandwidth memory interface to the nearest memory
controller, allowing for greatly increased bandwidth compared to the
standard system bus. We introduce region-based data processing, which
allows to trigger operations by merely storing data and is especially
suitable for large many-core designs. We show two different approaches to
trigger the architecture for near-memory operations, one using interrupts
for software-assisted processing and one directly interfacing with the
hardware accelerator. ... mehrOur evaluations show a performance gain of 72% on
SAD kernels, with memory performance improved by 48%. Benchmarking
AES encryption, we can show a speed up of 70%.