Zhewei Jiang
ABSTRACT
Online unsupervised spike sorting or clustering is an integral component of implantable closed-loop brain-computer-interface systems. Robust clustering performance against various non-idealities such as poor initialization and order-of-arrival of inputs are desirable while meeting the minimal area and power requirements for implants. We explore an online and unsupervised spike-sorting algorithm utilizing a low-overhead feature screening process that improves feature discriminability in the use of sub-optimal features for reducing hardware complexity. Based on the algorithm, an accelerator architecture that performs feature screening and clustering is devised and implemented in a 65-nm high-VTH CMOS, largely improving clustering accuracy even with poor clustering initialization. In the post-layout static timing and power simulation, the power consumption and the area of the accelerator are found to be 2.17 μW/ch and 0.052 μm2/ch, respectively, which are 53% and 25% smaller than the previous designs, while achieving the required throughput of 420 sorting/s at the supply voltage of 300mV.
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Index Terms
- A low power unsupervised spike sorting accelerator insensitive to clustering initialization in sub-optimal feature space
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