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Proving Flow Security of Sequential Logic via Automatically-Synthesized Relational Invariants.

, , and . CSF, page 420-435. IEEE Computer Society, (2017)

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MARVEL: A Decoupled Model-driven Approach for Efficiently Mapping Convolutions on Spatial DNN Accelerators., , , , , , and . CoRR, (2020)Architecting a Secure Wireless Network-on-Chip., , , and . NOCS, page 16:1-16:8. IEEE, (2018)A Formalism of DNN Accelerator Flexibility., , , , and . SIGMETRICS (Abstracts), page 53-54. ACM, (2022)Dataflow-Architecture Co-Design for 2.5D DNN Accelerators using Wireless Network-on-Package., , , , and . CoRR, (2020)NonGEMM Bench: Understanding the Performance Horizon of the Latest ML Workloads with NonGEMM Workloads., , , and . CoRR, (2024)XRBench: An Extended Reality (XR) Machine Learning Benchmark Suite for the Metaverse., , , , , , , , , and 8 other author(s). MLSys, mlsys.org, (2023)Proving Flow Security of Sequential Logic via Automatically-Synthesized Relational Invariants., , and . CSF, page 420-435. IEEE Computer Society, (2017)mRNA: Enabling Efficient Mapping Space Exploration for a Reconfiguration Neural Accelerator., , , , , and . ISPASS, page 282-292. IEEE, (2019)DREAM: A Dynamic Scheduler for Dynamic Real-time Multi-model ML Workloads., , , , , , and . ASPLOS (4), page 73-86. ACM, (2023)Understanding Reuse, Performance, and Hardware Cost of DNN Dataflow: A Data-Centric Approach., , , , , and . MICRO, page 754-768. ACM, (2019)