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Found 3 papers, 1 papers with code
Combined Scheduling, Memory Allocation and Tensor Replacement for Minimizing Off-Chip Data Accesses of DNN Accelerators
We propose an optimization framework, named COSMA, for mapping DNNs to an accelerator that finds the optimal operator schedule, memory allocation and tensor replacement that minimizes the additional data accesses.
In Search for a SAT-friendly Binarized Neural Network Architecture
Analyzing the behavior of neural networks is one of the most pressing challenges in deep learning.
Instruction-Level Abstraction (ILA): A Uniform Specification for System-on-Chip (SoC) Verification
In contrast to the pre-accelerator era, when the ISA played an important role in verification by enabling a clean separation of concerns between software and hardware, verification of these "accelerator-rich" SoCs presents new challenges.
Hardware Architecture
