Parallel hardware devices (eg, graphics processor units) have limited high-bandwidth
memory capacity. This negatively impacts the training of deep neural networks (DNNs) by …

Graphics processing units (GPUs) are moving towards supporting concurrent kernel
execution where multiple kernels may be co-executed on the same GPU and even on the …

Simulating all threads in a scaled GPU workload results in prohibitive simulation cost. Cycle-
level simulation is orders of magnitude slower than native silicon, the only solution is to …

The register file is one of the largest and most power-hungry structures in a Graphics
Processing Unit (GPU), because massive multithreading requires all the register state for …

GPGPUs use the Single-Instruction-Multiple-Thread (SIMT) execution model where a group
of threads—wavefront or warp—execute instructions in lockstep. When threads in a group …

With the skyrocketing advances of process technology, the increased need to process huge
amount of data, and the pivotal need for power efficiency, the usage of Graphics Processing …

Supply voltage reduction is an effective approach to significantly reduce GPU energy
consumption. As the largest on-chip storage structure, the GPU register file becomes the …

The GPU has provide higher throughput by integrating more execution resources into a
single chip without unduly compromising power efficiency. With the power wall challenge …

Graphics Processing Units (GPUs) have become an attractive platform for accelerating
challenging applications on a range of platforms, from High Performance Computing (HPC) …

Accelerators, such as GPUs, have proven to be highly successful in reducing execution time
and power consumption of compute-intensive applications. Even though they are already …