Recent neural network research has demonstrated a significant benefit in machine learning compared to conventional algorithms based on handcrafted models and features. In regions such as video, speech and image recognition, the neural network is now widely adopted. But the high complexity of neural network inference in computation and storage poses great differences on its application. These networks are computer-intensive algorithms that currently require the execution of dedicated hardware. In this case, we point out the difficulty of Adders (MOAs) and their high-resource utilization in a CNN implementation of FPGA .to address these challenge a parallel self-time adder is implemented which mainly aims at minimizing the amount of transistors and estimating different factors for PASTA, i.e. field, power, delay.
Purcell-Enhanced and Indistinguishable Single-Photon Generation from Quantum Dots Coupled to On-Chip Integrated Ring Resonators
