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.

翻译：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领域的波达方向/到达角估计算法。然而，MUSIC算法计算成本高昂，在低功耗硬件中实现具有挑战性，需要权衡精度、成本和功耗。本文提出MUSIC-lite，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。该方法特别应用于正交频分复用雷达用例中MUSIC算法计算密集的奇异值分解组件。MUSIC-lite将近似加法器集成到用于MUSIC硬件实现的迭代CORDIC算法中，从而产生具有研究价值的精度-面积-功耗权衡方案。实验结果表明，在实现高效MUSIC算法时，MUSIC-lite能以仅0.14%的误差为代价，平均节省17.25%的芯片面积和19.4%的功耗。