embedded device manufacturers

Abstract: We show that a hardwired distributed processing unit (HDPU) can take a neural decision at the sensor and so remove the inference from the host. Built for zebra-crossing detection on a low-cost Artix-7 FPGA, the unit decides on every camera frame in a fixed 2,269,477 clock cycles (34.04 ms depends upon Fpga clk ), inside the 37.49-ms frame period, and on the board it returns the same scores and decisions as its bit-exact software model on all 639 test images. The whole detection path, from camera pixels to decision, takes 1,814 LUTs, with no processor, no operating system and no full-resolution frame store. When the unit answers instead of reporting "not sure", 95.7% of its answers on street photographs are correct, and a hardware contrast stage lifts accuracy on camera-like dashcam frames from 47.7% to 78.2%. The host is spared all of the inference: 8.13 ms of a core in every frame period, where a full-precision ResNet-18 would need more than a frame period even on 16 threads. It also receives 16 bytes per frame instead of 614,400, 38,400 times less. The unit is an 80x60-pixel pipeline feeding a four-layer binarized CNN with 93,408 one-bit weights, verified bit-exactly from a Python model through RTL simulation to the board. Earlier HDPUs did pre-processing (edges, corners, steering); this one shows that the decision itself can move to the sensor.

Abstract: Securing bootloaders is critical to establishing the trusted computing base of modern systems. TianoCore's edk2, the de facto UEFI-based bootloader, is widely deployed but has recently been shown to contain numerous high-severity vulnerabilities. Existing approaches either focus on post-boot interfaces or rely on heavyweight simulation, limiting their scalability, throughput, and coverage of boot-time components. We present Metis, an automated technique for fuzzing edk2 protocols (i.e., boot-time interfaces) by rehosting them as user-space applications. Our key insight is that effective fuzzing must exercise not only protocol interfaces but also their dependency interactions, which serve as additional input channels. Metis employs a Direct-dependency Component Rehosting (DCR)-based strategy to construct semantically meaningful mocks for dependencies, enabling controlled input injection across all channels. It further leverages a hybrid approach combining static analysis and Large Language Model (LLM)-guided synthesis to generate and refine fuzzing harnesses. We implement Metis on the Host Based Firmware Analyzer (HBFA) framework and evaluate it on 60 drivers covering 162 protocols. Metis achieves 73.1% coverage, approximately 1.7 times higher than FuzzUEr, the state-of-the-art edk2 fuzzing technique, and discovers 137 previously unknown bugs. We have already reported 26 of them, including 12 that are remotely triggerable, and all reported vulnerabilities have been acknowledged by the vendors.