This work proposes a prototype system for utilizing nitrogen-vacancy center-based quantum sensing for generalized threat detection systems. Changes to the operation or environment of an IC will cause differences in the magnetic field emanations, which can be detected through changes in a spin-state-dependent photocurrent within a diamond. Threat detection circuitry can be integrated within the sensitive CMOS IC itself at a high spatial resolution for real-time monitoring and spatially resolved low-overhead protections. The key contributions of this work are the CMOS and NV center system for high magnetometer sensitivity while maintaining CMOS design flexibility, the novel security application for quantum sensing, and the proposed method of heterogeneous integration for a complete system.

We demonstrate Lightning, a reconfigurable photonic-electronic deep learning smartNIC that serves real-time inference requests at 4.055 GHz compute frequency. To do so, Lightning uses a novel datapath to feed traffic from the NIC into its photonic computing cores without incurring digital data movement bottlenecks. Lightning achieves this by employing a reconfigurable count-action abstraction, which decouples the compute control plane from the data plane. The count-action abstraction counts the number of operations for each computation task in the Directed Acyclic Graph (DAG). It then triggers the execution of the next task(s) as soon as the previous task is finished without interrupting the dataflow. Our prototype shows that Lightning achieves 99.25% photonic MAC accuracy. When serving real-time inference requests, Lightning accelerates the end-to-end inference latency of the LeNet DNN by 9.4× and 6.6× compared to Nvidia P4 and A100 GPUs, respectively.

The massive growth of machine learning-based applications and the end of Moore's law have created a pressing need to redesign computing platforms. We propose Lightning, the first reconfigurable photonic-electronic smartNIC to serve real-time deep neural network inference requests. Lightning uses a fast datapath to feed traffic from the NIC into the photonic domain without creating digital packet processing and data movement bottlenecks. To do so, Lightning leverages a novel reconfigurable count-action abstraction that keeps track of the required computation operations of each inference packet. Our count-action abstraction decouples the compute control plane from the data plane by counting the number of operations in each task and triggers the execution of the next task(s) without interrupting the dataflow. We evaluate Lightning's performance using four platforms: a prototype, chip synthesis, emulations, and simulations. Our prototype demonstrates the feasibility of performing 8-bit photonic multiply-accumulate operations with 99.25% accuracy. To the best of our knowledge, our prototype is the highest-frequency photonic computing system, capable of serving real-time inference queries at 4.055 GHz end-to-end. Our simulations with large DNN models show that compared to Nvidia A100 GPU, A100X DPU, and Brainwave smartNIC, Lightning accelerates the average inference serve time by 337×, 329×, and 42×, while consuming 352×, 419×, and 54× less energy, respectively.

We propose quantum tree network (QTN) designs that are 1) free from congestion in multiflow communications, 2) optimal routing with only local information (self-information) and 3) completely covers 2D surface, 4) with only logarithmic overhead. If the channel is dominated by the insertion loss and the communication is limited by the local gates, as in current state-of-the-art technologies, the overhead is log(N), while this is relaxed to the general cases with poly-log overhead by engaging repeater chains in the long range communication.