| IP명 | Energy Efficient Distance Computing: K-Means Clustering with Approximate Mixed-signal Design | ||
|---|---|---|---|
| Category | Analog | Application | AI |
| 실설계면적 | 4㎛ X 2㎛ | 공급 전압 | 1V |
| IP유형 | Hard IP | 동작속도 | 100MHz |
| 검증단계 | Silicon | 참여공정 | SS28-2201 |
| IP개요 | Distance computation between two input vectors is a widely used computing unit in several pattern recognition, signal processing and neuromorphic applications. However, the implementation of such a functionality in conventional MOS design requires expensive hardware and involves significant power consumption. Even power-efficient current-mode analog designs have proved to be slower and vulnerable to variations. In this paper, we propose an approximate mixed-signal design for the distance computing core by noting the fact that a vast majority of the signal processing applications involving this operation are resilient to small approximations in the distance computation. The proposed mixedsignal design is able to interface with external digital CMOS logic and simultaneously exhibit fast operating speeds. Another important feature of the proposed design is that the computing core is able to compute two variants of the distance metric, namely the (i) Euclidean distance squared (L22 norm) and (ii) Manhattan distance (L1 norm). The performance of the proposed design was evaluated on a standard K-means clustering algorithm on the “Iris flower dataset”. The result indicate a throughput of 6 ns per classification and ~2.3× lower energy consumption. |
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