{"repo":"yonseicasl/NeuroSpector","free":true,"listed":false,"github":"https://github.com/yonseicasl/NeuroSpector","clone":"git clone https://github.com/yonseicasl/NeuroSpector.git","description":"NeuroSpector: Dataflow and Mapping Optimizer for Deep Neural Network Accelerators","language":"C++","stars":23,"topics":["accelerator","deep-learning","deep-neural-network","hardware-accelerator","machine-learning","scheduling","architecture","dataflow","mapping","optimization"],"license":null,"category":"machine-learning","readme_excerpt":"NeuroSpector: Dataflow and Mapping Optimizer for Deep Neural Network Accelerators Developed by Chanho Park, Bogil Kim, Sungmin Ryu, and William J. Song\\ Computer Architecture and Systems Lab, Yonsei University\\ Current release: v1.5 (May 2024) Table of Contents 1. Intoduction 2. Compile 3. Run 4. Download 5. Reference Introduction A number of hardware accelerators have been proposed to speed up deep neural network (DNN) computations and enhance energy efficiency. DNN accelerators offer high-throughput and energy-efficient computing solutions by deploying many processing elements (PEs) and chips in parallel and exploiting data reuse across multiple levels of the memory hierarchy in the accelerators. The vertical and spatial arrangements of buffers and PEs construct a multi-level hierarchy of accelerator components from multiply-accumulate (MAC) units to global buffers and off-chip DRAM. For diverse DNN workload configurations and accelerator implementations, it is a highly challenging problem to find the proper ways of executing neural layers on accelerators to maximize energy efficiency and performance. The challenge lies in that hardware specifications (e.g., number of PEs and chips, buffer sizes and types) associated with workload configurations (e.g., width, height, channel, batch size) produce a huge number of possible dataflow and mapping options that can be exercised in an accelerator. NeuroSpector is a scheduling optimization framework that systematically analyzes the ","default_branch":null,"files":null,"tree":[],"storefront":"/r/yonseicasl","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/yonseicasl/NeuroSpector/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}