  Recent trends in deep learning (DL) imposed hardware accelerators as the most
viable solution for several classes of high-performance computing (HPC)
applications such as image classification, computer vision, and speech
recognition. This survey summarizes and classifies the most recent advances in
designing DL accelerators suitable to reach the performance requirements of HPC
applications. In particular, it highlights the most advanced approaches to
support deep learning accelerations including not only GPU and TPU-based
accelerators but also design-specific hardware accelerators such as FPGA-based
and ASIC-based accelerators, Neural Processing Units, open hardware
RISC-V-based accelerators and co-processors. The survey also describes
accelerators based on emerging memory technologies and computing paradigms,
such as 3D-stacked Processor-In-Memory, non-volatile memories (mainly,
Resistive RAM and Phase Change Memories) to implement in-memory computing,
Neuromorphic Processing Units, and accelerators based on Multi-Chip Modules.
Among emerging technologies, we also include some insights into quantum-based
accelerators and photonics. To conclude, the survey classifies the most
influential architectures and technologies proposed in the last years, with the
purpose of offering the reader a comprehensive perspective in the rapidly
evolving field of deep learning.
