Optimizing Scrubbing by Netlist Analysis for FPGA Configuration Bit Classification and Floorplanning

Existing scrubbing techniques for SEU mitigation on FPGAs do not guarantee an error-free operation after SEU recovering if the affected configuration bits do belong to feedback loops of the implemented circuits. In this paper, we a) provide a netlist-based circuit analysis technique to distinguish so-called critical configuration bits from essential bits in order to identify configuration bits which will need also state-restoring actions after a recovered SEU and which not. Furthermore, b) an alternative classification approach using fault injection is developed in order to compare both classification techniques. Moreover, c) we will propose a floorplanning approach for reducing the effective number of scrubbed frames and d), experimental results will give evidence that our optimization methodology not only allows to detect errors earlier but also to minimize the Mean-Time-To-Repair (MTTR) of a circuit considerably. In particular, we show that by using our approach, the MTTR for datapath-intensive circuits can be reduced by up to 48.5% in comparison to standard approaches

The benefits and costs of netlist randomization based side-channel countermeasures: an in-depth evaluation

Exchanging FPGA-based implementations of cryptographic algorithms during run-time using netlist randomized versions has been introduced recently as a unique countermeasure against side channel attacks. Using partial reconfiguration, it is possible to shuffle between structurally different but functionally similar versions of a cryptographic implementation. The resulting varying power profile enhances the resistance against power-based side channel attacks. While side channel leakage is reduced, costs in terms of additional resources and/or lowered throughput are often increased due to the overheads of the required online partial reconfiguration. In this work, we provide an in-depth evaluation of the leakage-area-throughput trade-off

A self-adaptive SEU mitigation system for FPGAs with an internal block RAM radiation particle sensor

In this paper, we propose a self-adaptive FPGA- based, partially reconfigurable system for space missions in order to mitigate Single Event Upsets in the FPGA configuration and fabric. Dynamic reconfiguration is used here for an on-demand replication of modules in dependence of current and changing radiation levels. More precisely, the idea is to trigger a redundancy scheme such as Dual Modular Redundancy or Triple Modular Redundancy in response to a continuously monitored Single Event Upset rate measured inside the on-chip memories itself, e.g., any subset (even used) internal Block RAMs. Depending on the current radiation level, the minimal number of replicas is determined at run- time under the constraint that a required Safety Integrity Level for a module is ensured and configured accordingly. For signal processing applications it is shown that this autonomous adaption to the different solar conditions realizes a resource efficient mitigation. In our case study, we show that it is possible to triplicate the data throughput at the Solar Maximum condition (no flares) compared to a Triple Modular Redundancy implementation of a single module. We also show the decreasing Probability of Failures Per Hour by 2 × 104 at flare-enhanced conditions compared with a non-redundant system. Our work is a part of the In-Orbit Verification of the Heinrich Hertz communication satellite

A dynamic reconfigurable architecture for hybrid spiking and convolutional FPGA-based neural network designs

This work presents a dynamically reconfigurable architecture for Neural Network (NN) accelerators implemented in Field-Programmable Gate Array (FPGA) that can be applied in a variety of application scenarios. Although the concept of Dynamic Partial Reconfiguration (DPR) is increasingly used in NN accelerators, the throughput is usually lower than pure static designs. This work presents a dynamically reconfigurable energy-efficient accelerator architecture that does not sacrifice throughput performance. The proposed accelerator comprises reconfigurable processing engines and dynamically utilizes the device resources according to model parameters. Using the proposed architecture with DPR, different NN types and architectures can be realized on the same FPGA. Moreover, the proposed architecture maximizes throughput performance with design optimizations while considering the available resources on the hardware platform. We evaluate our design with different NN architectures for two different tasks. The first task is the image classification of two distinct datasets, and this requires switching between Convolutional Neural Network (CNN) architectures having different layer structures. The second task requires switching between NN architectures, namely a CNN architecture with high accuracy and throughput and a hybrid architecture that combines convolutional layers and an optimized Spiking Neural Network (SNN) architecture. We demonstrate throughput results from quickly reprogramming only a tiny part of the FPGA hardware using DPR. Experimental results show that the implemented designs achieve a 7× faster frame rate than current FPGA accelerators while being extremely flexible and using comparable resources

Design of the Magnet System of the Neutron Decay Facility PERC

The PERC (Proton and Electron Radiation Channel) facility is currently under construction at the research reactor FRM II, Garching. It will serve as an intense and clean source of electrons and protons from neutron beta decay for precision studies. It aims to contribute to the determination of the Cabibbo-Kobayashi-Maskawa quark-mixing element $V_{ud}$ from neutron decay data and to search for new physics via new effective couplings. PERC's central component is a 12m long superconducting magnet system. It hosts an 8m long decay region in a uniform field. An additional high-field region selects the phase space of electrons and protons which can reach the detectors and largely improves systematic uncertainties. We discuss the design of the magnet system and the resulting properties of the magnetic field.Comment: Proceedings of the International Workshop on Particle Physics at Neutron Sources PPNS 2018, Grenoble, France, May 24-26, 201

New Directions for IP Core Watermarking and Identification

In this talk, we present watermarking and identification techniques for FPGA IP cores. Unlike most existing watermarking techniques, the focus of our techniques lies on ease of verification, even if the protected cores are embedded into a product. Moreover, we have concentrated on higher abstraction levels for embedding the watermark, particularly at the logic level, where IP cores are distributed as netlist cores. With the presented watermarking methods, it is possible to watermark IP cores at the logic level and identify them with a high likelihood and in a reproducible way in a purchased product from a company that is suspected to have committed IP fraud. The investigated techniques establish the authorship by verification of either an FPGA bitfile or the power consumption of a given FPGA