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NEUSORT2.0: A multiple-channel neural signal processor with systolic array buffer and channel-interleaving processing schedule

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4 Author(s)
Tung-Chien Chen ; University of California, Santa Cruz, USA ; Zhi Yang ; Wentai Liu ; Liang-Gee Chen

An emerging class of neuroprosthetic devices aims to provide aggressive performance by integrating more complicated signal processing hardware into the neural recording system with a large amount of electrodes. However, the traditional parallel structure duplicating one neural signal processor (NSP) multiple times for multiple channels takes a heavy burden on chip area. The serial structure sequentially switching the processing task between channels requires a bulky memory to store neural data and may has a long processing delay. In this paper, a memory hierarchy of systolic array buffer is proposed to support signal processing interleavingly channel by channel in cycle basis to match up with the data flow of the optimized multiple-channel frontend interface circuitry. The NSP can thus be tightly coupled to the analog frontend interface circuitry and perform signal processing for multiple channels in real time without any bulky memory. Based on our previous one-channel NSP of NEUSORT1.0 [1], the proposed memory hierarchy is realized on NEUSORT2.0 for a 16-channel neural recording system. Compared to 16 of NEUSORT1.0, NEUSORT2.0 demonstrates a 81.50% saving in terms of area×power factor.

Published in:

2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Date of Conference:

20-25 Aug. 2008