A 65-nm CMOS Lossless Bio-Signal Compression Circuit With 250 FemtoJoule Performance Per Bit

• Published in: IEEE transactions on biomedical circuits and systems Vol. 13; no. 5; pp. 1087 - 1100
• Main Authors: Crispin-Bailey, Christopher, Dai, Chenglaing, Austin, Jim
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Biomedical measurement; Circuit design; CMOS; Compression; Compression algorithms; Compression ratio; Compression tests; Data Compression; Data storage; Data transmission; ECG; EEG; Encoding; Energy conversion efficiency; Gating; Hardware; Humans; Integrated circuits; lossless data compression; Optimization; power efficiency; Signal Processing, Computer-Assisted; Storage capacity; Very large scale integration; VLSI design; wearable sensors
• DOI: 10.1109/TBCAS.2019.2938672
• ISSN: 1932-4545

A 65 nm CMOS integrated circuit implementation of a bio-physiological signal compression device is presented, reporting exceptionally low power, and extremely low silicon area cost, relative to state-of-the-art. A novel `xor-log2-sub-band' data compression scheme is evaluated, achieving modest compression, but with very low resource cost. With the intent to design the 'simplest useful compression algorithm', the outcome is demonstrated to be very favourable where power must be saved by trading off compression effort against data storage capacity, or data transmission power, even where more complex algorithms can deliver higher compression ratios. A VLSI design and fabricated Integrated Circuit implementation are presented, and estimated performance gains and efficiency measures for various bio-medical use-cases are given. Power costs as low as 1.2 pJ per sample-bit are suggested for a 10 kSa/s data-rate, whilst utilizing a power-gating scenario, and dropping to 250 fJ/bit at continuous conversion data-rates of 5 MSa/sec. This is achieved with a diminutive circuit area of 155 um 2 . Both power and area appear to be state-of-the-art in terms of compression versus resource cost, and this yields benefit for system optimization.