PowerGenie: Analytically-Guided Evolutionary Discovery of Superior Reconfigurable Power Converters

Discovering superior circuit topologies requires navigating an exponentially large design space-a challenge traditionally reserved for human experts. Existing AI methods either select from predefined templates or generate novel topologies at a limited scale without rigorous verification, leaving large-scale performance-driven discovery underexplored. We present PowerGenie, a framework for automated discovery of higher-performance reconfigurable power converters at scale. PowerGenie introduces: (1) an automated analytical framework that determines converter functionality and theoretical performance limits without component sizing or SPICE simulation, and (2) an evolutionary finetuning method that co-evolves a generative model with its training distribution through fitness selection and uniqueness verification. Unlike existing methods that suffer from mode collapse and overfitting, our approach achieves higher syntax validity, function validity, novelty rate, and figure-of-merit (FoM). PowerGenie discovers a novel 8-mode reconfigurable converter with 23% higher FoM than the best training topology. SPICE simulations confirm average absolute efficiency gains of 10% across 8 modes and up to 17% at a single mode. Code is available at https://github.com/xz-group/PowerGenie.

Compressive Sensing Photoacoustic Imaging Receiver with Matrix-Vector-Multiplication SAR ADC

Wearable photoacoustic imaging devices hold great promise for continuous health monitoring and point-of-care diagnostics. However, the large data volume generated by high-density transducer arrays presents a major challenge for realizing compact and power-efficient wearable systems. This paper presents a photoacoustic imaging receiver (RX) that embeds compressive sensing directly into the hardware to address this bottleneck. The RX integrates 16 AFEs and four matrix-vector-multiplication (MVM) SAR ADCs that perform energy- and area-efficient analog-domain compression. The architecture achieves a 4-8x reduction in output data rate while preserving low-loss full-array information. The MVM SAR ADC executes passive and accurate MVM using user-defined programmable ternary weights. Two signal reconstruction methods are implemented: (1) an optimization approach using the fast iterative shrinkage-thresholding algorithm, and (2) a learning-based approach employing implicit neural representation. Fabricated in 65 nm CMOS, the chip achieves an ADC's SNDR of 57.5 dB at 20.41 MS/s, with an AFE input-referred noise of 3.5 nV/sqrt(Hz). MVM linearity measurements show R^2 > 0.999 across a wide range of weights and input amplitudes. The system is validated through phantom imaging experiments, demonstrating high-fidelity image reconstruction under up to 8x compression. The RX consumes 5.83 mW/channel and supports a general ternary-weighted measurement matrix, offering a compelling solution for next-generation miniaturized, wearable PA imaging systems.