Due to the demonstrated superiority, dynamic neural networks (NNs), which adapt their network structures to different inputs, have been recognized as an optimized alternative to conventional static NNs. However, researchers have not explored the implications of dynamic NN on neural processing unit (NPU) architecture design. Consequently, we analyze the characteristics and inefficient sources of executing dynamic NNs on existing hardware. From our analysis, existing NPUs, designed for static NNs, cannot effectively handle the execution of dynamic operator and agent-dependent data loading in dynamic NNs. To this end, we present DNA, an efficient accelerator optimized to deal with the challenges of running general dynamic NNs. Firstly, to improve the execution efficiency of dynamic operators, we propose a transverter-based online scheduling strategy to rapidly generate efficient scheduling for each dynamic operator. Secondly, to mitigate hardware idleness caused by the non-deterministic and agent-dependent data access patterns in dynamic NNs, we propose a novel predictor-based prefetching strategy that achieves effective data preloading with negligible cost. We implemented our accelerator, DNA, by integrating an additional online scheduler into a typical many-core baseline accelerator. According to our evaluation of various dynamic NNs, DNA achieves $3.48\boldsymbol\times$ speedup and $3.03\boldsymbol\times$ energy savings over the baseline accelerator.

Quantization is one of the most hardware-efficient ways to reduce inference costs for deep neural network (DNN) models. Nevertheless, with the continuous increase of DNN model sizes (240× in two years) and the emergence of large language models, existing static quantization methods fail to utilize the sparsity and redundancy of models sufficiently. Motivated by the pervasive dynamism in data tensors across DNN models, we propose a dynamic precision quantization algorithm to further reduce computational costs beyond statically quantized DNN models. Furthermore, we find that existing precision-flexible accelerators cannot support the DNN models with dynamic precision. To this end, we design a novel accelerator, Drift, and achieve online scheduling to efficiently support dynamic precision execution. We conduct experiments with various DNN models, including CNN-based and Transformer-based models. Evaluation results show that Drift achieves 2.85× speedup and 3.12× energy saving compared to existing precision-flexible accelerators with statically quantized models.

Efficient deep learning models, especially optimized for edge devices, benefit from low inference latency to efficient energy consumption. Two classical techniques for efficient model inference are lightweight neural architecture search (NAS), which automatically designs compact network models, and quantization, which reduces the bit-precision of neural network models. As a consequence, joint design for both neural architecture and quantization precision settings is becoming increasingly popular. There are three main aspects that affect the performance of the joint optimization between neural architecture and quantization: 1) quantization precision selection (QPS); 2) quantization-aware training (QAT); and 3) NAS. However, existing works focus on at most twofold of these aspects, and result in secondary performance. To this end, we proposed a novel automatic optimization framework, DAQU, that allows jointly searching for Pareto-optimal neural architecture and quantization precision combination among more than $10^{47}$ quantized subnet models. To overcome the instability of the conventional automatic optimization framework, DAQU incorporates a warm-up strategy to reduce the accuracy gap among different neural architectures, and a precision-transfer training approach to maintain flexibility among different quantization precision settings. Our experiments show that the quantized lightweight neural networks generated by DAQU consistently outperform state-of-the-art NAS and quantization joint optimization methods.