Tackling the X-ray cargo inspection challenge using machine learning

The current infrastructure for non-intrusive inspection of cargo containers cannot accommodate exploding com-merce volumes and increasingly stringent regulations. There is a pressing need to develop methods to automate parts of the inspection workflow, enabling expert operators to focus on a manageable number of high-risk images. To tackle this challenge, we developed a modular framework for automated X-ray cargo image inspection. Employing state-of-the-art machine learning approaches, including deep learning, we demonstrate high performance for empty container verification and specific threat detection. This work constitutes a significant step towards the partial automation of X-ray cargo image inspection

System level synthesis of dataflow programs: HEVC decoder case study

International audienceWhile dealing with increasing complexity of signal processing algorithms, the primary motivation for the development of High-Level Synthesis (HLS) tools for the automatic generation of Register Transfer Level (RTL) description from high-level description language is the reduction of time-to-market. However, most existing HLS tools operate at the component level, thus the entire system is not taken into consideration. We provide an original technique that raises the level of abstraction to the system level in order to obtain RTL description from a dataflow description. First, we design image processing algorithms using an actor oriented language under the Reconfigurable Video Coding (RVC) standard. Once the design is achieved, we use a dataflow compilation infrastructure called Open RVC-CAL Compiler (Orcc) to generate a C-based code. Afterward, a Xilinx HLS tool called Vivado is used for an automatic generation of synthesizable hardware implementation. In this paper, we show that a simulated hardware code generation of High Efficiency Video Coding (HEVC) under the RVC specifications is rapidly obtained with promising preliminary results

TREBUCHET: Fully Homomorphic Encryption Accelerator for Deep Computation

Secure computation is of critical importance to not only the DoD, but across financial institutions, healthcare, and anywhere personally identifiable information (PII) is accessed. Traditional security techniques require data to be decrypted before performing any computation. When processed on untrusted systems the decrypted data is vulnerable to attacks to extract the sensitive information. To address these vulnerabilities Fully Homomorphic Encryption (FHE) keeps the data encrypted during computation and secures the results, even in these untrusted environments. However, FHE requires a significant amount of computation to perform equivalent unencrypted operations. To be useful, FHE must significantly close the computation gap (within 10x) to make encrypted processing practical. To accomplish this ambitious goal the TREBUCHET project is leading research and development in FHE processing hardware to accelerate deep computations on encrypted data, as part of the DARPA MTO Data Privacy for Virtual Environments (DPRIVE) program. We accelerate the major secure standardized FHE schemes (BGV, BFV, CKKS, FHEW, etc.) at >=128-bit security while integrating with the open-source PALISADE and OpenFHE libraries currently used in the DoD and in industry. We utilize a novel tile-based chip design with highly parallel ALUs optimized for vectorized 128b modulo arithmetic. The TREBUCHET coprocessor design provides a highly modular, flexible, and extensible FHE accelerator for easy reconfiguration, deployment, integration and application on other hardware form factors, such as System-on-Chip or alternate chip area