Self-Partial and Dynamic Reconfiguration Implementation for AES using FPGA

This paper addresses efficient hardware/software implementation approaches for the AES (Advanced Encryption Standard) algorithm and describes the design and performance testing algorithm for embedded system. Also, with the spread of reconfigurable hardware such as FPGAs (Field Programmable Gate Array) embedded cryptographic hardware became cost-effective. Nevertheless, it is worthy to note that nowadays, even hardwired cryptographic algorithms are not so safe. From another side, the self-reconfiguring platform is reported that enables an FPGA to dynamically reconfigure itself under the control of an embedded microprocessor. Hardware acceleration significantly increases the performance of embedded systems built on programmable logic. Allowing a FPGA-based MicroBlaze processor to self-select the coprocessors uses can help reduce area requirements and increase a system's versatility. The architecture proposed in this paper is an optimal hardware implementation algorithm and takes dynamic partially reconfigurable of FPGA. This implementation is good solution to preserve confidentiality and accessibility to the information in the numeric communication

The artificial retina processor for track reconstruction at the LHC crossing rate

We present results of an R&D study for a specialized processor capable of precisely reconstructing, in pixel detectors, hundreds of charged-particle tracks from high-energy collisions at 40 MHz rate. We apply a highly parallel pattern-recognition algorithm, inspired by studies of the processing of visual images by the brain as it happens in nature, and describe in detail an efficient hardware implementation in high-speed, high-bandwidth FPGA devices. This is the first detailed demonstration of reconstruction of offline-quality tracks at 40 MHz and makes the device suitable for processing Large Hadron Collider events at the full crossing frequency.Comment: 4th draft of WIT proceedings modified according to JINST referee's comments. 10 pages, 6 figures, 2 table

Comprehensive Evaluation of OpenCL-based Convolutional Neural Network Accelerators in Xilinx and Altera FPGAs

Deep learning has significantly advanced the state of the art in artificial intelligence, gaining wide popularity from both industry and academia. Special interest is around Convolutional Neural Networks (CNN), which take inspiration from the hierarchical structure of the visual cortex, to form deep layers of convolutional operations, along with fully connected classifiers. Hardware implementations of these deep CNN architectures are challenged with memory bottlenecks that require many convolution and fully-connected layers demanding large amount of communication for parallel computation. Multi-core CPU based solutions have demonstrated their inadequacy for this problem due to the memory wall and low parallelism. Many-core GPU architectures show superior performance but they consume high power and also have memory constraints due to inconsistencies between cache and main memory. FPGA design solutions are also actively being explored, which allow implementing the memory hierarchy using embedded BlockRAM. This boosts the parallel use of shared memory elements between multiple processing units, avoiding data replicability and inconsistencies. This makes FPGAs potentially powerful solutions for real-time classification of CNNs. Both Altera and Xilinx have adopted OpenCL co-design framework from GPU for FPGA designs as a pseudo-automatic development solution. In this paper, a comprehensive evaluation and comparison of Altera and Xilinx OpenCL frameworks for a 5-layer deep CNN is presented. Hardware resources, temporal performance and the OpenCL architecture for CNNs are discussed. Xilinx demonstrates faster synthesis, better FPGA resource utilization and more compact boards. Altera provides multi-platforms tools, mature design community and better execution times

Adaptable Security in Wireless Sensor Networks by Using Reconfigurable ECC Hardware Coprocessors

Specific features of Wireless Sensor Networks (WSNs) like the open accessibility to nodes, or the easy observability of radio communications, lead to severe security challenges. The application of traditional security schemes on sensor nodes is limited due to the restricted computation capability, low-power availability, and the inherent low data rate. In order to avoid dependencies on a compromised level of security, a WSN node with a microcontroller and a Field Programmable Gate Array (FPGA) is used along this work to implement a state-of-the art solution based on ECC (Elliptic Curve Cryptography). In this paper it is described how the reconfiguration possibilities of the system can be used to adapt ECC parameters in order to increase or reduce the security level depending on the application scenario or the energy budget. Two setups have been created to compare the software- and hardware-supported approaches. According to the results, the FPGA-based ECC implementation requires three orders of magnitude less energy, compared with a low power microcontroller implementation, even considering the power consumption overhead introduced by the hardware reconfiguratio

Boundary Condition Adjustment Methods And Systems

Methods and systems for reactor lattice depletion are disclosed. One exemplary method, among others, comprises the steps of defining a reactor eigenvalue, the reactor eigenvalue being a specified ratio of actual neutron production to loss in the reactor; producing a lattice eigenvalue, the lattice eigenvalue being an estimated ratio of neutron production to loss in the lattice; and adjusting a boundary condition of the lattice to cause convergence of the lattice eigenvalue and the reactor eigenvalue in order to produce at least one physics parameter.Georgia Tech Research Corporatio

6502 emulator on FPGA

6502 microprocessor was once used in almost all of the microcomputer in the 80s, including the Apple II lines of computer, the Commodore PET, the Commodore 64, the Atari 8-bit series and even on the Nintendo Entertainment System (NES) video game console. The objective of this project is to emulate the once famous 6502 microprocessor onto a FPGA chip. The FPGA-based 6502 microprocessor had to emulate the functionality of a real 6502 microprocessor. Accurate pinouts emulation is desired but not a must. The 6502 assembly language is easy to learn and building a computer based on this microprocessor requires very few parts, thus making this project a great experiential learning process. The scope of this project requires the student to have an in-depth understanding on computer system architecture, especially on 6502 architecture; V erilog to understand existing 6502 source code from Bird Computer and also FPGA development process (synthesis tools) to transfer the Verilog code to the FPGA chip. Thus far, the resources and information on 6502 microprocessor looks promising. The student earlier scope was to come up with the 6502 code in Verilog HDL, but as there is available code from Bird Computer (State Machine coded) so the student had chanced his objectives to understand the existing code and implement it on FPGA only. But as along the way, problems occur on hardware implementation, focus had been switched again to simulate the existing code or ALU or simple processor to build up student understanding and for documentation for future project expansion. To test the functionality of the 6502 system, the student will either find existing application or come up with simple program to run using the FPGA-based 6502 system

FPGA-based all-digital multi-protocol RFID reader

No abstract available.Not Publishe