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.
In-Memory Computing (IMC) has been widely studied to mitigate data transfer bottlenecks in von Neumann architectures. Recently proposed IMC circuit topologies dramatically reduce data transfer requirements by performing various operations such as Multiply-Accumulate (MAC) inside the memory. In this paper, we present an SRAM macro designed for accelerating Memory-Augmented Neural Network (MANN). We first propose algorithmic optimizations for a few-shot learning algorithm employing MANN for efficient hardware implementation. Then, we present an SRAM macro that efficiently accelerates the algorithm by realizing key operations such as L1 distance calculation and Winner-Take-All (WTA) operation through mixed-signal computation circuits. Fabricated in 40nm LP CMOS technology, the design demonstrates 27.7 TOPS/W maximum energy efficiency, while achieving 93.40% and 98.28% classification accuracy for 5-way 1-shot and 5-way 5-shot learning on the Omniglot dataset, which closely matches the accuracy of the baseline algorithm.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.