Sampling Spiking Neural Network Electronic Nose on a Tiny-Chip
電子鼻運用SSNN算法進行化學品分類識別研究
Hoda S. Abdel-Aty-Zohdy
Microelectronics & Bio-Inspired Systemd Design
Department of electrical and Computer Engineering
Oakland University, Rochester, MI, USA
Jacob N. Allen
Microelectronics & Bio-Inspired Systemd Design
Department of electrical and Computer Engineering
Oakland University , Rochester, MI, USA
Abstract
Chemicals classification using a new Sampling Spiking Neural Network (SSNN) approach is presented in this paper with experimental measurements using the Cyranose 320 sensor array. The network is unique in its minimal yet powerful design which implements on chip learning and parallel monitoring to detect
binary odor patterns with high noise environment. The SSNN architecture is further implemented on a 0.5 um CMOS technology tiny-chip designed to work in conjunction with a 256K external SRAM memory. It handles the routing of spike signal among 32,000 synapses and 255 neurons. At the same time, it tracks and records learning statistics. The chip can be used in parallel with other SSNN co processors for very large systems. Experimental measurements of our SSNN E-Nose classifier, compared to other E-nose systems proved superior in capability, size, and correctness.
本文提出了一種新的神經網絡(SSNN)方法對化學品進行分類識別，并利用Cyranose 320電子鼻進行了實驗測量。該網絡的*之處在于其小但功能強大的設計，實現了片上學習和并行監控以檢測高噪聲環境下的二元氣味模式。SSNN架構進一步實現在0.5umCMOS技術的微型芯片上，該芯片設計為與256K外部SRAM存儲器協同工作。它處理32000個突觸和255個神經元之間的尖峰信號路由。同時，對學習統計數據進行跟蹤記錄。對于非常大的系統，該芯片可以與其他SSNN協處理器并行使用。與其他電子鼻系統相比，我們的SSNN電子鼻分類器的實驗測量結果在性能、尺寸和正確性方面都表現出了*性。
