| Title | Self-Healing Approach for Hardware Neural Network Architecture |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Khalil, Kasem, Eldash, Omar, Kumar, Ashok, Bayoumi, Magdy |
| Conference Name | 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS) |
| Date Published | aug |
| Keywords | Altira 10 GX FPGA, artificial neural network, Biological neural networks, complex neural network, composability, current self-healing neural network, Embryonic hardware, Evolvable hardware, fault detection, fault tolerant computing, Fault-tolerant, faulty node, field programmable gate arrays, Hardware, hardware description languages, hardware neural Network architecture, hardware nodes, modest area overhead, neighbor node, neural chips, neural net architecture, Neurons, pubcrawl, Redundancy, resilience, Resiliency, self-healing, self-healing approach results, self-healing networks, spare node, Training, VHDL |
| Abstract | Neural Network is used in many applications and guarding its performance against faults is a research challenge. Self-healing neural network is a promising concept for achieving reliability, which is the ability to detect and fix a fault in the system automatically. Most of the current self-healing neural network are based on replication of hardware nodes which causes significant area overhead. The proposed self-healing approach results in a modest area overhead and it is suitable for complex neural network. The proposed method is based on a shared operation and a spare node in each layer which compensates for any faulty node in the layer. Each faulty node will be compensated by its neighbor node, and the neighbor node performs the faulty node as well as its own operations sequentially. In the case the neighbor is faulty, the spare node will compensate for it. The proposed method is implemented using VHDL and the simulation results are obtained using Altira 10 GX FPGA for a different number of nodes. The area overhead is very small for a complex network. The reliability of the proposed method is studied and compared with the traditional neural network. |
| DOI | 10.1109/MWSCAS.2019.8885235 |
| Citation Key | khalil_self-healing_2019 |
Self-Healing Approach for Hardware Neural Network Architecture