An Energy-Efficient In-Sensor Computing Architecture With In-Pixel Spiking Neuron and Event-Driven Computation for Low-Energy X-Ray Imaging: Modeling and Evaluation

The use of neural networks for processing X-ray detector data has become a key development trend in high-energy physics and medical imaging. As detector arrays grow, handling the resulting massive data volumes poses significant challenges in terms of system bandwidth and power consumption. To address the bandwidth bottleneck and high analog-to-digital conversion (ADC) power consumption in conventional X-ray imaging systems, we propose an in-sensor computing architecture, SpikeX, for accelerating spiking neural networks (SNN). First, a photon-counting analog front end is directly integrated with pixel-level SNN neurons, enabling the first convolutional layer to be computed using spike signals. This eliminates the need for analog-to-digital conversion, significantly reducing power and bandwidth requirements. Next, an event-driven on-chip SNN accelerator performs efficient temporal inference, enabling an end-to-end pipeline from signal acquisition to image classification. Furthermore, detector non-idealities can be absorbed by the SNN model during training. To systematically investigate these effects, we develop a signal processing algorithm, enabling effective correction of detector non-idealities. To validate the proposed architecture, we implemented the SpikeX using a 180 nm CMOS process, featuring a$28\times 28$pixel array and 32 Leaky Integrate-and-Fire (LIF) processing units. Simulation results show that, at a clock frequency of 50 MHz, the architecture achieves an energy efficiency of 1.88 TOPS/W, significantly outperforming comparable state-of-the-art designs.