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In ultrasound imaging systems, a large number of waveforms are acquired in parallel from a transducer array. To facilitate the move to portable ultrasound systems with real-time displays, we implemented a low-power digital beamformer ASIC in 65 nm bulk CMOS technology. We describe three operating modes that provide a run-time tradeoff between image quality and system power consumption. A sliding window approach eliminates the need for an on-chip SRAM, which reduces area and power. The chip generates four output pixels per clock cycle from eight channel of input data, allowing 30 frames per second at 1.92 MHz. The prototype test chip is operational down to a core supply voltage of 0.49 V, with a measured power of 185 uW in real-time operation at 0.52 V.

As the lateral resolution and axial imaging depth of a ultrasound image increase, so do the number of transducer elements and the corresponding processing units. Conventional ultrasound systems are often large and expensive due to the intensive requirement of large beamforming arrays. But for applications such as point-of-care diagnostics in rural areas, the movement to a portable and low-power ultrasound imaging system is warranted. To address the low power requirement for portable applications, the proposed beamformer operates in one of three modes, namely quarter, half, and full resolution modes, selectable at runtime. In the first two modes, a subset of elements, with effective pitches of 4x and 2x of the transducer pitch, are processed for low quality images of the full region of interest. These modes provide run-time power reduction, because the Analog Front End (AFE) and Analog-to-Digital Converter (ADC) for the unused channels can be turned off by the feedback control signals from the beamformer. Based on the low quality image, the user can intuitively specify a smaller region where a higher quality image is desired, and the full resolution beamforming mode is used. A system energy model is set up to evaluate the image quality and frame rate performance, while providing realistic power consumption predictions. The model offers fast and accurate behavioral level understanding of the ultrasound system under optimization.