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.

This paper studies the synergies between memory corruption vulnerabilities and speculative execution vulnerabilities. We leverage speculative execution attacks to bypass an important memory protection mechanism, ARM Pointer Authentication, a security feature that is used to enforce pointer integrity. We present PACMAN, a novel attack methodology that speculatively leaks PAC verification results via micro-architectural side channels without causing any crashes. Our attack removes the primary barrier to conducting control-flow hijacking attacks on a platform protected using Pointer Authentication.