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1. One Weight Bitwidth to Rule Them All

   Chin, Ting-Wu; Chuang, Pierce; Chandra, Vikas; Marculescu, Diana (August 2020, European Conference on Computer Vision Workshops)

   Weight quantization for deep ConvNets has shown promising results for applications such as image classification and semantic segmentation and is especially important for applications where memory storage is limited. However, when aiming for quantization without accuracy degradation, different tasks may end up with different bitwidths. This creates complexity for software and hardware support and the complexity accumulates when one considers mixed-precision quantization, in which case each layer’s weights use a different bitwidth. Our key insight is that optimizing for the least bitwidth subject to no accuracy degradation is not necessarily an optimal strategy. This is because one cannot decide optimality between two bitwidths if one has smaller model size while the other has better accuracy. In this work, we take the first step to understand if some weight bitwidth is better than others by aligning all to the same model size using a width-multiplier. Under this setting, somewhat surprisingly, we show that using a single bitwidth for the whole network can achieve better accuracy compared to mixed-precision quantization targeting zero accuracy degradation when both have the same model size. In particular, our results suggest that when the number of channels becomes a target hyperparameter, a single weight bitwidth throughout the network shows superior results for model compression. 

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2. Optimize Deep Convolutional Neural Network with Ternarized Weights and High Accuracy

   https://doi.org/10.1109/WACV.2019.00102  

   He, Zhezhi; Gong, Boqing; Fan, Deliang (January 2019, 2019 IEEE Winter Conference on Applications of Computer Vision (WACV))

   Deep convolution neural network has achieved great success in many artificial intelligence applications. However, its enormous model size and massive computation cost have become the main obstacle for deployment of such powerful algorithm in the low power and resource limited embedded systems. As the countermeasure to this problem, in this work, we propose statistical weight scaling and residual expansion methods to reduce the bit-width of the whole network weight parameters to ternary values (i.e. -1, 0, +1), with the objectives to greatly reduce model size, computation cost and accuracy degradation caused by the model compression. With about 16X model compression rate, our ternarized ResNet-32/44/56 could outperforms full-precision counterparts by 0.12%, 0.24% and 0.18% on CIFAR-10 dataset. We also test our ternarization method with AlexNet and ResNet-18 on ImageNet dataset, which both achieve the best top-1 accuracy compared to recent similar works, with the same 16X compression rate. If further incorporating our residual expansion method, compared to the full-precision counterpart, our ternarized ResNet-18 even improves the top-5 accuracy by 0.61% and merely degrades the top-1 accuracy only by 0.42% for ImageNet dataset, with 8X model compression rate. It outperforms the recent ABC-Net by 1.03% in top-1 accuracy and 1.78% in top-5 accuracy, with around 1.25X higher compression rate and more than 6X computation reduction due to the weight sparsity. 

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3. Harmonious Coexistence of Structured Weight Pruning and Ternarization for Deep Neural Networks

   https://doi.org/10.1609/aaai.v34i04.6138  

   Yang, Li; He, Zhezhi; Fan, Deliang (June 2020, Proceedings of the AAAI Conference on Artificial Intelligence)

   Deep convolutional neural network (DNN) has demonstrated phenomenal success and been widely used in many computer vision tasks. However, its enormous model size and high computing complexity prohibits its wide deployment into resource limited embedded system, such as FPGA and mGPU. As the two most widely adopted model compression techniques, weight pruning and quantization compress DNN model through introducing weight sparsity (i.e., forcing partial weights as zeros) and quantizing weights into limited bit-width values, respectively. Although there are works attempting to combine the weight pruning and quantization, we still observe disharmony between weight pruning and quantization, especially when more aggressive compression schemes (e.g., Structured pruning and low bit-width quantization) are used. In this work, taking FPGA as the test computing platform and Processing Elements (PE) as the basic parallel computing unit, we first propose a PE-wise structured pruning scheme, which introduces weight sparsification with considering of the architecture of PE. In addition, we integrate it with an optimized weight ternarization approach which quantizes weights into ternary values ({-1,0,+1}), thus converting the dominant convolution operations in DNN from multiplication-and-accumulation (MAC) to addition-only, as well as compressing the original model (from 32-bit floating point to 2-bit ternary representation) by at least 16 times. Then, we investigate and solve the coexistence issue between PE-wise Structured pruning and ternarization, through proposing a Weight Penalty Clipping (WPC) technique with self-adapting threshold. Our experiment shows that the fusion of our proposed techniques can achieve the best state-of-the-art ∼21× PE-wise structured compression rate with merely 1.74%/0.94% (top-1/top-5) accuracy degradation of ResNet-18 on ImageNet dataset. 

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4. Dynamic Neural Network to Enable Run-Time Trade-off between Accuracy and Latency

   https://doi.org/10.1145/3394885.3431628  

   Yang, Li; Fan, Deliang (January 2021, 2021 26th Asia and South Pacific Design Automation Conference (ASP-DAC))

   null (Ed.)

   To deploy powerful deep neural network (DNN) into smart, but resource limited IoT devices, many prior works have been proposed to compress DNN to reduce the network size and computation complexity with negligible accuracy degradation, such as weight quantization, network pruning, convolution decomposition, etc. However, by utilizing conventional DNN compression methods, a smaller, but fixed, network is generated from a relative large background model to achieve resource limited hardware acceleration. However, such optimization lacks the ability to adjust its structure in real-time to adapt for a dynamic computing hardware resource allocation and workloads. In this paper, we mainly review our two prior works [13], [15] to tackle this challenge, discussing how to construct a dynamic DNN by means of either uniform or non-uniform sub-nets generation methods. Moreover, to generate multiple non-uniform sub-nets, [15] needs to fully retrain the background model for each sub-net individually, named as multi-path method. To reduce the training cost, in this work, we further propose a single-path sub-nets generation method that can sample multiple sub-nets in different epochs within one training round. The constructed dynamic DNN, consisting of multiple sub-nets, provides the ability to run-time trade-off the inference accuracy and latency according to hardware resources and environment requirements. In the end, we study the the dynamic DNNs with different sub-nets generation methods on both CIFAR-10 and ImageNet dataset. We also present the run-time tuning of accuracy and latency on both GPU and CPU. 

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5. Toward Weight Sharing Paradigm for Efficient AI: Training and Inference Serving

   https://doi.org/10.1145/3759441.3759447  

   Behnam, Payman; Khare, Alind; Garg, Dhruv; Tumanov, Alexey (August 2025, ACM SIGOPS Operating Systems Review)

   Deep neural networks are increasingly required to operate across diverse hardware platforms, latency constraints, and power budgets, which motivates the need for specialized models for each scenario. However, designing and training a separate model per scenario or serving a large ensemble of models is often impractical. Weight sharing has emerged as a promising paradigm to address this challenge by training a single ''SuperNet'' that subsumes many sub-models (SubNets), and by reusing weights across those SubNets both at training and inference time. This paper provides an abridged survey of our recent advances that leverage weight sharing for efficient AI, covering both training and inference serving. In centralized once-for-all training, Delayed ε-Shrinking (DεS) improves training efficiency by strategically scheduling the introduction of smaller SubNets during training. In a federated fashion, SuperFedNas co-trains a SuperNet across distributed clients and disjoins training and searching, which enables oneshot specialization to many deployment targets at minimal cost. ∇QDARTS integrates quantization into differentiable architecture search, jointly finding neural architectures, weights, and low-precision settings to yield highly efficient models in a single search. For inference serving, SuperServe introduces a weight-shared model with dynamic SubNet routing (SubNetAct) to instantaneously switch among a spectrum of accuracy-latency operating points, coupled with a scheduler (SlackFit) for unpredictable workloads. Finally, SUSHI co-designs model, system, and accelerator to exploit weightshared SuperNets on tinyML devices, caching SubGraphs on FPGA to reduce latency and energy. Together, these works demonstrate that the weight sharing paradigm can dramatically improve the efficiency of both training and inference serving of deep models across a range of scenarios. 

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