  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.
