Algorithm-Driven Architectural Design Space Exploration of Domain-Specific Medical-Sensor Processors

作者：Shoaib Mohammed^*; Jha Niraj K; Verma Naveen

来源：IEEE Transactions on Very Large Scale Integration Systems, 2013, 21(10): 1849-1862.

DOI：10.1109/TVLSI.2012.2220161

• Biomedical sensor processors
• classification accelerators
• embedded machine learning
• low-energy design by voltage and precision scaling
• structured hardware specialization
• support-vector machines

Data-driven machine-learning techniques enable the modeling and interpretation of complex physiological signals. The energy consumption of these techniques, however, can be excessive, due to the complexity of the models required. In this paper, we study the tradeoffs and limitations imposed by the energy consumption of high-order detection models implemented in devices designed for intelligent biomedical sensing. Based on the flexibility and efficiency needs at various processing stages in data-driven biomedical algorithms, we explore options for hardware specialization through architectures based on custom instruction and coprocessor computations. We identify the limitations in the former, and propose a coprocessor-based platform that exploits parallelism in computation as well as voltage scaling to operate at a subthreshold minimum-energy point. We present results from post-layout simulation of cardiac arrhythmia detection with patient data from the MIT-BIH database. After wavelet-based feature extraction, which consumes 12.28 mu J, we demonstrate classification computations in the 12.00-120.05 mu J range using 10 000-100 000 support vectors. This represents 1170x lower energy than that of a low-power processor with custom instructions alone. After morphological feature extraction, which consumes 8.65 mu J of energy, the corresponding energy numbers are 10.24-24.51 mu J, which is 1548x smaller than one based on a custom-instruction design. Results correspond to V-dd = 0.4 V and a data precision of 8 b.

• 出版日期2013-10