MUSIC-lite：使用近似计算的高效MUSIC算法：一个OFDM雷达案例研究
MUSIC-lite: Efficient MUSIC using Approximate Computing: An OFDM Radar Case Study
AI总结：
针对MUSIC算法在低功耗硬件中实现的计算成本高问题，提出MUSIC-lite，通过近似计算探索精度-面积-功耗权衡，针对OFDM雷达中MUSIC的SVD组件，将近似加法器融入CORDIC算法，实现平均节省17.25%面积、19.4%功耗且误差仅0.14%。
摘要：
多重信号分类（MUSIC）是一种广泛应用于自动驾驶、医学成像和天文学等多个领域的波达方向（DoA）/到达角（AoA）估计算法。然而，MUSIC计算成本高昂，难以在低功耗硬件中实现，需要探索精度、成本和功耗之间的权衡。我们提出MUSIC-lite，它利用近似计算生成一个探索精度-面积-功耗权衡的设计空间。这一方法专门应用于正交频分复用（OFDM）雷达用例中MUSIC算法计算密集的奇异值分解（SVD）组件。MUSIC-lite将近似加法器融入用于MUSIC硬件实现的迭代CORDIC算法中，生成了具有价值的精度-面积-功耗权衡。我们的实验表明，MUSIC-lite能够为高效MUSIC实现节省平均17.25%的片上面积和19.4%的功耗，同时仅产生0.14%的最小误差。
英文摘要：
Multiple Signal Classification (MUSIC) is a widely used Direction of Arrival (DoA)/Angle of Arrival (AoA) estimation algorithm applied to various application domains such as autonomous driving, medical imaging, and astronomy. However, MUSIC is computationally expensive and challenging to implement in low-power hardware, requiring exploration of trade-offs between accuracy, cost, and power. We present MUSIC-lite, which exploits approximate computing to generate a design space exploring accuracy-area-power trade-offs. This is specifically applied to the computationally intensive singular value decomposition (SVD) component of the MUSIC algorithm in an orthogonal frequency-division multiplexing (OFDM) radar use case. MUSIC-lite incorporates approximate adders into the iterative CORDIC algorithm that is used for hardware implementation of MUSIC, generating interesting accuracy-area-power trade-offs. Our experiments demonstrate MUSIC-lite's ability to save an average of 17.25% on-chip area and 19.4% power with a minimal 0.14% error for efficient MUSIC implementations.
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