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Chapter 7 therefore proposes a design space exploration method of energy-efficient hardware accelerators for mixed-precision DNNs used for real-time ...
We propose an evolutionary-based co-optimization strategy by considering three metrics: DNN accuracy, execution latency, and power consumption.
The efficiency of deep neural network (DNN) solutions on real hardware devices are mainly decided by the DNN architecture and the compiler-level scheduling ...
In this paper, we propose UNICO, a Unified Co-Optimization framework for hardware-software co-design, aimed at addressing the efficiency issues of vast design ...
Dec 14, 2024 · The efficiency of deep neural network (DNN) solutions on real hardware devices are mainly decided by the DNN architecture and the ...
In this thesis, we present algorithm-hardware co-optimization approaches to address the challenges of efficient DNN deployments from three aspects.
Missing: execution. | Show results with:execution.
Nov 23, 2023 · We present an end-to-end co-design approach encompassing computer architecture, training algorithm, and inference optimization to efficiently execute networks
Experimental results show that the co-optimization outperforms the optimization of DNN for fixed hardware configuration with up to 53% hardware efficiency gains ...
This paper proposes an algorithm–hardware co-optimization and deployment method for FPGA-based CNN remote sensing image processing.
In this paper, we propose. UNICO, a Unified Co-Optimization framework for hardware-software co-design, aimed at addressing the efficiency issues of vast design ...