Researchers at National Taiwan University are developing low-complexity neural network technologies using quantization to reduce model size while maintaining accuracy. Their work includes binary-weighted CNNs and transformers, along with a neural architecture search scheme (TPC-NAS) applied to image recognition, object detection, and NLP tasks. They have also built a PE-based CNN/transformer hardware accelerator in Xilinx FPGA SoC with a PyTorch-based software framework. Why it matters: This research provides practical methods for deploying efficient deep learning models on resource-constrained hardware, potentially enabling broader adoption of AI in embedded systems and edge devices.
This paper analyzes the impact of device uncertainties on deep neural networks (DNNs) in emerging device-based Computing-in-memory (CiM) systems. The authors propose UAE, an uncertainty-aware Neural Architecture Search scheme, to identify DNN models robust to these uncertainties. The goal is to mitigate accuracy drops when deploying trained models on real-world platforms.
This article discusses the reliability of Deep Neural Networks (DNNs) and their hardware platforms, especially regarding soft errors caused by cosmic rays. It highlights that while DNNs are robust against bit flips, errors can still lead to miscalculations in AI accelerators. The talk, led by Prof. Masanori Hashimoto from Kyoto University, will cover identifying vulnerabilities in neural networks and reliability exploration of AI accelerators for edge computing. Why it matters: As DNNs are deployed in safety-critical applications in the region, ensuring the reliability of AI hardware is crucial for safe and trustworthy operation.
This research introduces a novel method using the Lateral Accretive Hybrid Network (LEARNet) to capture and analyze micro-expressions for mental health applications. The method refines both broad and subtle facial cues to detect mental health conditions like anxiety or depression. The authors also propose a neural architecture search (NAS) strategy to design a compact CNN for micro-expression recognition, improving performance and resource use. Why it matters: By integrating micro-emotion recognition with mental health estimation, the approach enables more accurate and early detection of emotional and mental health issues, potentially leading to improved well-being.
The paper introduces MedNNS, a neural network search framework designed for medical imaging, addressing challenges in architecture selection and weight initialization. MedNNS constructs a meta-space encoding datasets and models based on their performance using a Supernetwork-based approach, expanding the model zoo size by 51x. The framework incorporates rank loss and Fréchet Inception Distance (FID) loss to capture inter-model and inter-dataset relationships, improving alignment in the meta-space and outperforming ImageNet pre-trained DL models and SOTA NAS methods.