TY  - CONF
ID  - SisLab3725
UR  - https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3725/
A1  - Nguyen, Duy Anh
A1  - Bui, Duy Hieu
A1  - Iacopi, Francesca
A1  - Tran, Xuan Tu
Y1  - 2019/09/03/
N2  - Deep Neural Networks (DNNs) have been successfully
applied to various real-world machine learning applications.
However, performing large DNN inference tasks in real-time
remains a challenge due to its substantial computational costs. Recently, Spiking Neural Networks (SNNs) have emerged as an alternative way of processing DNN?s task. Due to its eventbased, data-driven computation, SNN reduces both inference latency and complexity. With efficient conversion methods from traditional DNN, SNN exhibits similar accuracy, while leveraging many state-of-the-art network models and training methods. In this work, an efficient neuromorphic hardware architecture for image recognition task is presented. To preserve accuracy, the analog-to-spiking conversion algorithm is adopted. The system aims to minimize hardware area cost and power consumption, enabling neuromorphic hardware processing in edge devices. Simulation results have shown that, with the MNIST digit recognition task, the system has achieved ×20 reduction in terms
of core area cost compared to the state-of-the-art works, with an accuracy of 94.4%, core area of 15 ?m2 at a maximum frequency of 250 MHz.
TI  - An Efficient Event-driven Neuromorphic Architecture for Deep Spiking Neural Networks
M2  - Singapore
AV  - restricted
T2  - 2019 32nd IEEE International System-on-Chip Conference (SOCC)
ER  -