AI RESEARCH
Hardware-Algorithm Co-Optimization of Early-Exit Neural Networks for Multi-Core Edge Accelerators
arXiv CS.CV
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ArXi:2512.04705v2 Announce Type: replace-cross Deployment of dynamic neural networks on edge accelerators requires careful consideration of hardware constraints beyond conventional complexity metrics such as Multiply-Accumulate operations. In Early-Exiting Neural Networks (EENN), exit placement, quantization level, and hardware workload mapping interact in non-trivial ways, influencing memory traffic, accelerator utilization, and ultimately energy-latency trade-offs.