This paper presents a spike sorting processor based on an accurate spike clustering algorithm. The proposed spike sorting algorithm employs an L2-normalized convolutional autoencoder to extract features from the input, where the autoencoder is trained using the proposed spike sorting-aware loss. In addition, we propose a similarity-based K-means clustering algorithm that conditionally updates the means by observing the cosine similarity. The modified K-means algorithm exhibits better convergence and enables online clustering with higher classification accuracy. We implement a spike sorting processor based on the proposed algorithm using an efficient timemultiplexed hardware architecture in a 40-nm CMOS process. Experimental results show that the processor consumes 224.75µW/mm 2 when processing 16 input channels at 7.68MHz and 0.55V. Our design achieves 95.54% clustering accuracy, outperforming prior spike sorting processor designs.
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