… a number of neural network accelerators to achieve high … accelerators. In summary,
this review can serve as a reference for hardware researchers in the area of neural networks…

… advances in accelerator designs for deep neural networks (DNNs)—that is, DNN accelerators.
… of computing units, dataflow optimization, targeted network topologies, and so forth. This …

… flexible interconnection, the DNN accelerator can support different … accelerator design.
This paper systematically investigates the interconnection networks in modern DNN accelerator …

… for designing FPGA-based accelerators for CNNs. … accelerators. Section 6 reviews techniques
for simplifying CNN models which helps in reducing HW overhead of FPGA accelerators. …

… In this paper, existing DNN hardware accelerators are … hardware accelerators helps to
identify the best accelerator model … Detailed reviews on analog neural network accelerators can …

… accelerators for DNN prediction. This paper presents a holistic methodology to automate the
design of DNN accelerators … consumption for different neural network implementations, as …

… accelerators … a neural network hardware accelerator. After being hyped in the 1990s, then
fading away for almost two decades, there is a surge of interest in hardware neural networks …

… Compared with a state-of-the-art neural network accelerator, DianNao, our accelerator
achieves 7.23x and 6.43x better performance and energy efficiency respectively. …

… neural networks to the particle accelerator community and report on some work in neural
network … of the challenges of particle accelerator control, highlight recent advances in neural …

… approximate units for DNN accelerators as well as accuracy … Computing for DNN accelerators
can go beyond energy … /Adders in neural network accelerators has attracted significant …