As general-purpose processors have hit the power wall and chip fabrication cost escalates
alarmingly, coarse-grained reconfigurable architectures (CGRAs) are attracting increasing …

Deep convolutional neural networks (CNNs) are widely used in modern AI systems for their
superior accuracy but at the cost of high computational complexity. The complexity comes …

Reconfigurable architectures have gained popularity in recent years as they allow the
design of energy-efficient accelerators. Fine-grain fabrics (eg FPGAs) have traditionally …

Industry is increasingly turning to reconfigurable architectures like FPGAs and CGRAs for
improved performance and energy efficiency. Unfortunately, adoption of these architectures …

Energy consumed for transferring data across the processor memory hierarchy constitutes a
large fraction of total system energy consumption, and this fraction has steadily increased …

The authors demonstrate the key role dataflows play in the optimization of energy efficiency
for deep neural network (DNN) accelerators. By introducing a systematic approach to …

Recent high-level synthesis and accelerator-related architecture papers show a great
disparity in workload selection. To improve standardization within the accelerator research …

Demand for low-power data processing hardware continues to rise inexorably. Existing
programmable and" general purpose" solutions (eg. SIMD, GPGPUs) are insufficient, as …

Domain-specific hardware accelerators can provide orders of magnitude speedup and
energy efficiency over general purpose processors. However, they require extensive manual …

Coarse-grain reconfigurable arrays (CGRAs) can achieve much higher performance and
efficiency than general-purpose cores, approaching the performance of a specialized design …