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MEMTI: Optimizing On-Chip Nonvolatile Storage for Visual Multitask Inference at the Edge.

, , , and . IEEE Micro, 39 (6): 73-81 (2019)

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Demystifying Bayesian Inference Workloads., , , , , and . ISPASS, page 177-189. IEEE, (2019)FlexGibbs: Reconfigurable Parallel Gibbs Sampling Accelerator for Structured Graphs., , , , and . FCCM, page 334. IEEE, (2019)MLPerf Training Benchmark., , , , , , , , , and 24 other author(s). CoRR, (2019)Accelerating Bayesian Inference on Structured Graphs Using Parallel Gibbs Sampling., , , , and . FPL, page 159-165. IEEE, (2019)SMAUG: End-to-End Full-Stack Simulation Infrastructure for Deep Learning Workloads., , , , , and . CoRR, (2019)2017 International Symposium on Computer Architecture Influential Paper Award.. IEEE Micro, 37 (6): 90-91 (2017)Benchmarking TPU, GPU, and CPU Platforms for Deep Learning., , and . CoRR, (2019)Learning Syntax from Function Words., and . ICGI, volume 3264 of Lecture Notes in Computer Science, page 273-274. Springer, (2004)SMIV: A 16-nm 25-mm² SoC for IoT With Arm Cortex-A53, eFPGA, and Coherent Accelerators., , , , , and . IEEE J. Solid State Circuits, 57 (2): 639-650 (2022)MEMTI: Optimizing On-Chip Nonvolatile Storage for Visual Multitask Inference at the Edge., , , and . IEEE Micro, 39 (6): 73-81 (2019)