Spiking neural networks (SNNs) have been widely used in artificial intelligence applications. Resistive random-access memory (RRAM) based neuromorphic hardware can be used for low-power and high-speed inference of SNNs. However, RRAM devices suffer from stuck-at-fault (SAF) defects due to the immature fabrication process. SAF defects can lead to incorrect weights of SNNs and thus severely degrade the inference accuracy. In this paper, we propose a fault-tolerance synaptic-to-RRAM mapping scheme to deploy SNNs while protecting the inference accuracy. We first explore how different weights affect the model accuracy in SNNs and find that the frequency of spikes plays a vital role. Motivated by this, we develop an SNN-oriented metric to evaluate the importance of weights. Then we propose a defect-aware mapping scheme based on a simulated-annealing framework to efficiently map synapses to RRAM to mitigate the impact of SAFs. Evaluation shows that the proposed strategy can improve the accuracy by 18.74% on average compared to existing mapping strategy.

Nonvolatile field-programmable gate arrays (NVFPGAs) can use multi-level cell (MLC) nonvolatile memories (NVMs) to enhance their logic density. However, the highdensity design of NVFPGAs degrades the intra-routability of configurable logic blocks (CLBs), which significantly prolongs the time consumed by the packing process in the computer-aided design (CAD) flow. To relieve the efficiency degradation, in this paper, we propose a routability-aware re-pair stage to adjust the logical-physical look-up table (LUT) assignments to mitigate the congestion and improve their intra-routability, thereby reducing the packing time. In addition, exploiting the structural equivalence of MLC LUTs, we remove unnecessary intra-routing attempts from packing to further improve efficiency. Evaluation shows the proposed strategies reduce packing time by $41.48 \%$ on average. Index Terms-nonvolatile memory (NVM), multi-level cell (MLC), field-programmable gate array (FPGA), computer-aided design (CAD), packing.

Nonvolatile field-programmable gate arrays (FPGAs) offer advantages in terms of high logic density and near-zero leakage power when contrasted with conventional static random access memory-based FPGAs. However, they have a lifetime issue. To deal with this problem, a series of configuration files can be generated with various logical-to-physical mappings. This enables intensive writing to be distributed across different physical regions for wear leveling. Currently, the configuration files are independently generated, which is time consuming. In this article, we propose to investigate correlations and use them to assist the computer-aided design (CAD) flow to speed up the procedure of generating configuration files. First, we develop dynamic probabilities to drive the swapping of placement stage in CAD flow, so as to push components to locate appropriate positions quickly. Second, we design the congestion information inheritance strategy to adjust routing parameters in the routing stage, aiming to reduce the number of routing attempts. Evaluation shows that the proposed schemes can deliver 44.15% decrease in placement and routing runtime, while maintaining comparable performance and lifetime, when compared with existing strategies.

Field-programmable gate arrays (FPGAs) have been widely used in artificial intelligence applications. As the capacity requirements of both computation and memory resources continuously increase, emerging nonvolatile memory has been proposed to replace static random access memory (SRAM) in FPGAs to build nonvolatile FPGAs (NV-FPGAs), which have advantages of high density and near-zero leakage power. Features of emerging nonvolatile memory should be fully explored to improve performance, energy efficiency as well as lifetime of NV-FPGAs. In this paper, we study an intrinsic characteristic of emerging nonvolatile memory, i.e., computing-in-memory, in nonvolatile block random access memory (BRAM) of NV-FPGAs. Specifically, we present a computational BRAM architecture (C-BRAM), and propose a computational density aware operator allocation strategy to fully utilize C-BRAM. Neural network inference is taken as an example to evaluate the proposed architecture and strategy, showing 68% and 62% improvement in computational density compared to traditional SRAM-based FPGA and existing NV-FPGA, respectively.

Nonvolatile field programmable gate arrays (NVFP-GAs) have been proposed to address the challenges raised by artificial intelligence and big data related applications, since nonvolatile memories (NVMs) introduce advantages of high storage density, low leakage power, and high system robustness. In addition, multi-level cell (MLC), which can store multiple bits within one memory cell, further improves the logic density of NVFPGAs. However, the inefficient write operation of MLC NVM significantly increases the reconfiguration cost in aspects of energy, latency, and lifetime. In this paper, we focus on the reconfiguration cost of MLC LUTs in NVFPGA and propose a lightweight input inversion based scheme to reduce the reconfiguration cost. Inversion flexibility is defined and modeled for LUT inputs to guide the proposed scheme. We also discuss how the proposed scheme can be combined with other existing write reduction strategies. Evaluation shows the proposed scheme can reduce reconfiguration cost by 10.01 % with negligible overhead.

Nonvolatile FPGAs have advantages of high density and near-zero leakage power compared with traditional SRAM-based FPGAs. However, they have lifetime issue. To deal with this problem, a series of configuration files can be generated with various logical-to-physical mappings so that intensive writes can be distributed to different physical regions for wear leveling. Currently, the configuration files are independently generated, which is time-consuming. In this paper, we propose to investigate correlations between components and use them to guide the computer-aided design (CAD) flow to speed up the procedure of deriving configuration files. Specifically, we develop dynamic probabilities to drive the swapping of placement step in the CAD flow to push components to locate appropriate positions quickly. Evaluation shows that the proposed schemes can deliver 36.32% reduction in number of swappings when compared with existing strategies, while maintaining comparable performance and lifetime.