Bitmessage 14884/14/2023 These findings are innovative because they combine three real effectsthat occur in SRAM-based FPGAs. Experimental results show that aging and voltage scaling can increase at least two times the susceptibility of SRAM-based FPGAs toSoft Error Rate (SER). Furthermore, the effects of aging and voltage scaling increment thesensitivity of SRAM-based FPGAs to soft errors. However, they are susceptible to radiation effects such as soft errors inthe configuration memory. SRAM-based FPGAs are attractive for critical applications due to their high perfor-mance and flexibility. Reliability is an important design constraint for critical applications at ground-level and aero-space. The enhanced performance of HPCFISBP has also been established through comparison with the state of the art techniques in terms of error correction coverage, error correction time, redundancy and residual error. HPCFISBP provides better bit error rate (BER) performance nearly by 20 dB and 10 dB compared to Hamming code and HPC respectively. , termed as ‘HPCFISBP’ to correct AMBUs in the configuration memory of FPGA without any modification in its basic architecture. In this paper we have proposed a simple and efficient error mitigation model combining Hence, interleaving among CFs are quite advantageous for mitigation of clustered errors in the configuration memory of FPGA. Configuration data of the FPGAs are composed of a number of configuration frames (CFs) and there is a high probability that multiple physically adjacent CFs may be affected by clustered error. Hence, efficient multi-bit error correcting codes with low redundancy is of utmost need to mitigate the effect of AMBUs. Commonly used error mitigation techniques in FPGA either have large overheads, complex decoding circuitry or are not very efficient to correct AMBUs. Radiated particles with high energy and low momentum may damage a group of adjacent logic cells and switches in reconfigurable devices, which can lead to clustered errors. In the present age of high density integrated circuits, radiation induced adjacent muti-bit upsets (AMBUs) or clustered errors are very prominent in the configuration memory of static random access memory (SRAM) based Field Programmable Gate Array (FPGA) devices. We highlight challenges and opportunities that the future holds in the area of system software technology for neuromorphic computing. ![]() Here, we provide a comprehensive overview of such frameworks proposed for both, platform-based design and hardware-software co-design. Consequently, there is a clear need for system software frameworks that can implement machine learning applications on current and emerging neuromorphic systems, and simultaneously address performance, energy, and reliability. Additionally, neuromorphic systems are required to guarantee real-time performance, consume lower energy, and provide tolerance to logic and memory failures. ![]() With the growing complexity on design and technology fronts, programming such systems to admit and execute a machine learning application is becoming increasingly challenging. Recently, both industry and academia have proposed several different neuromorphic systems to execute machine learning applications that are designed using Spiking Neural Networks (SNNs).
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