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DiagNNose: Toward Error Localization in Deep Learning Hardware-Based on VTA-TVM Stack., , , , и . IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 43 (1): 217-229 (января 2024)Approximate Systems: Synergistically Approximating Sensing, Computing, Memory, and Communication Subsystems for Energy Efficiency., и . Approximate Circuits, Springer, (2019)A Direct Trust dependent Link State Routing Protocol Using Route Trusts for WSNs (DTLSRP)., , и . Wirel. Sens. Netw., 3 (4): 125-134 (2011)Energy-Efficient IoT-Health Monitoring System using Approximate Computing., , и . Internet Things, (2020)qLUT: Input-Aware Quantized Table Lookup for Energy-Efficient Approximate Accelerators., и . ACM Trans. Embed. Comput. Syst., 16 (5s): 130:1-130:23 (2017)Energy-Efficient Edge Detection using Approximate Ramanujan Sums., , , , и . ISQED, стр. 414-420. IEEE, (2020)PreSyNC: Hardware realization of the Presynaptic Region of a Biologically Extensive Neuronal Circuitry., , , , , и . VLSID, стр. 228-233. IEEE, (2021)Special Session: Effective In-field Testing of Deep Neural Network Hardware Accelerators., , , и . VTS, стр. 1-4. IEEE, (2022)Energy-Efficient Approximate Edge Inference Systems., , и . ACM Trans. Embed. Comput. Syst., 22 (4): 77:1-77:50 (июля 2023)Analysis and Mitigation of DRAM Faults in Sparse-DNN Accelerators., , , , и . IEEE Des. Test, 40 (2): 90-99 (апреля 2023)