Cocco: Hardware-Mapping Co-Exploration towards Memory Capacity-Communication Optimization
Zhanhong Tan, Zijian Zhu, Kaisheng Ma
2024年份
9被引次数
3顶会引用
摘要
Memory is a critical design consideration in current data-intensive DNN accelerators, as it profoundly determines energy consumption, bandwidth requirements, and area costs. As DNN structures become more complex, a larger on-chip memory capacity is required to reduce data movement overhead, but at the expense of silicon costs. Some previous works have proposed memory-oriented optimizations, such as different data reuse and layer fusion schemes. However, these methods are not general and potent enough to cope with various graph structures.
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引用它的顶会 Paper3
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- FlashMem: Supporting Modern DNN Workloads on Mobile with GPU Memory Hierarchy OptimizationsZhihao Shu, Md. Musfiqur Rahman Sanim, Hangyu Zheng, Kunxiong Zhu 等ASPLOS 2026
它引用的顶会 Paper14
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- Mind mappings: enabling efficient algorithm-accelerator mapping space searchKartik Hegde, Po-An Tsai, Sitao Huang, Vikas Chandra 等ASPLOS 2021 · 被引用 95 次
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