MUSIC-lite: Efficient MUSIC using Approximate Computing: An OFDM Radar Case Study
<|reference_start|>MUSIC-lite: Efficient MUSIC using Approximate Computing: An OFDM Radar Case Study: 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.<|reference_end|>
@article{bhattacharjya2024music-lite:,
    title={MUSIC-lite: Efficient MUSIC using Approximate Computing: An OFDM Radar
  Case Study},
    author={Rajat Bhattacharjya, Arnab Sarkar, Biswadip Maity, Nikil Dutt},
    journal={arXiv preprint arXiv:2407.04849},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.04849},
    primaryClass={cs.AR cs.SD eess.AS}
}