Skip2-LoRA: A Lightweight On-device DNN Fine-tuning Method for Low-cost Edge Devices

This paper proposes Skip2-LoRA as a lightweight fine-tuning method for deep neural networks to address the gap between pre-trained and deployed models. In our approach, trainable LoRA (low-rank adaptation) adapters are inserted between the last layer and every other layer to enhance the network expressive power while keeping the backward computation cost low. This architecture is well-suited to cache intermediate computation results of the forward pass and then can skip the forward computation of seen samples as training epochs progress. We implemented the combination of the proposed architecture and cache, denoted as Skip2-LoRA, and tested it on a $15 single board computer. Our results show that Skip2-LoRA reduces the fine-tuning time by 90.0% on average compared to the counterpart that has the same number of trainable parameters while preserving the accuracy, while taking only a few seconds on the microcontroller board.

A Tiny Supervised ODL Core with Auto Data Pruning for Human Activity Recognition

In this paper, we introduce a low-cost and low-power tiny supervised on-device learning (ODL) core that can address the distributional shift of input data for human activity recognition. Although ODL for resource-limited edge devices has been studied recently, how exactly to provide the training labels to these devices at runtime remains an open-issue. To address this problem, we propose to combine an automatic data pruning with supervised ODL to reduce the number queries needed to acquire predicted labels from a nearby teacher device and thus save power consumption during model retraining. The data pruning threshold is automatically tuned, eliminating a manual threshold tuning. As a tinyML solution at a few mW for the human activity recognition, we design a supervised ODL core that supports our automatic data pruning using a 45nm CMOS process technology. We show that the required memory size for the core is smaller than the same-shaped multilayer perceptron (MLP) and the power consumption is only 3.39mW. Experiments using a human activity recognition dataset show that the proposed automatic data pruning reduces the communication volume by 55.7% and power consumption accordingly with only 0.9% accuracy loss.

FPGA-Accelerated Correspondence-free Point Cloud Registration with PointNet Features

Point cloud registration serves as a basis for vision and robotic applications including 3D reconstruction and mapping. Despite significant improvements on the quality of results, recent deep learning approaches are computationally expensive and power-hungry, making them difficult to deploy on resource-constrained edge devices. To tackle this problem, in this paper, we propose a fast, accurate, and robust registration for low-cost embedded FPGAs. Based on a parallel and pipelined PointNet feature extractor, we develop custom accelerator cores namely PointLKCore and ReAgentCore, for two different learning-based methods. They are both correspondence-free and computationally efficient as they avoid the costly feature matching step involving nearest-neighbor search. The proposed cores are implemented on the Xilinx ZCU104 board and evaluated using both synthetic and real-world datasets, showing the substantial improvements in the trade-offs between runtime and registration quality. They run 44.08-45.75x faster than ARM Cortex-A53 CPU and offer 1.98-11.13x speedups over Intel Xeon CPU and Nvidia Jetson boards, while consuming less than 1W and achieving 163.11-213.58x energy-efficiency compared to Nvidia GeForce GPU. The proposed cores are more robust to noise and large initial misalignments than the classical methods and quickly find reasonable solutions in less than 15ms, demonstrating the real-time performance.

A Cost-Efficient FPGA Implementation of Tiny Transformer Model using Neural ODE

Transformer is an emerging neural network model with attention mechanism. It has been adopted to various tasks and achieved a favorable accuracy compared to CNNs and RNNs. While the attention mechanism is recognized as a general-purpose component, many of the Transformer models require a significant number of parameters compared to the CNN-based ones. To mitigate the computational complexity, recently, a hybrid approach has been proposed, which uses ResNet as a backbone architecture and replaces a part of its convolution layers with an MHSA (Multi-Head Self-Attention) mechanism. In this paper, we significantly reduce the parameter size of such models by using Neural ODE (Ordinary Differential Equation) as a backbone architecture instead of ResNet. The proposed hybrid model reduces the parameter size by 94.6% compared to the CNN-based ones without degrading the accuracy. We then deploy the proposed model on a modest-sized FPGA device for edge computing. To further reduce FPGA resource utilization, we quantize the model following QAT (Quantization Aware Training) scheme instead of PTQ (Post Training Quantization) to suppress the accuracy loss. As a result, an extremely lightweight Transformer-based model can be implemented on resource-limited FPGAs. The weights of the feature extraction network are stored on-chip to minimize the memory transfer overhead, allowing faster inference. By eliminating the overhead of memory transfers, inference can be executed seamlessly, leading to accelerated inference. The proposed FPGA implementation achieves 12.8x speedup and 9.21x energy efficiency compared to ARM Cortex-A53 CPU.

An FPGA-Based Accelerator for Graph Embedding using Sequential Training Algorithm

A graph embedding is an emerging approach that can represent a graph structure with a fixed-length low-dimensional vector. node2vec is a well-known algorithm to obtain such a graph embedding by sampling neighboring nodes on a given graph with a random walk technique. However, the original node2vec algorithm typically relies on a batch training of graph structures; thus, it is not suited for applications in which the graph structure changes after the deployment. In this paper, we focus on node2vec applications for IoT (Internet of Things) environments. To handle the changes of graph structures after the IoT devices have been deployed in edge environments, in this paper we propose to combine an online sequential training algorithm with node2vec. The proposed sequentially-trainable model is implemented on a resource-limited FPGA (Field-Programmable Gate Array) device to demonstrate the benefits of our approach. The proposed FPGA implementation achieves up to 205.25 times speedup compared to the original model on CPU. Evaluation results using dynamic graphs show that although the original model decreases the accuracy, the proposed sequential model can obtain better graph embedding that can increase the accuracy even when the graph structure is changed.

An Integrated FPGA Accelerator for Deep Learning-based 2D/3D Path Planning

Path planning is a crucial component for realizing the autonomy of mobile robots. However, due to limited computational resources on mobile robots, it remains challenging to deploy state-of-the-art methods and achieve real-time performance. To address this, we propose P3Net (PointNet-based Path Planning Networks), a lightweight deep-learning-based method for 2D/3D path planning, and design an IP core (P3NetCore) targeting FPGA SoCs (Xilinx ZCU104). P3Net improves the algorithm and model architecture of the recently-proposed MPNet. P3Net employs an encoder with a PointNet backbone and a lightweight planning network in order to extract robust point cloud features and sample path points from a promising region. P3NetCore is comprised of the fully-pipelined point cloud encoder, batched bidirectional path planner, and parallel collision checker, to cover most part of the algorithm. On the 2D (3D) datasets, P3Net with the IP core runs 24.54-149.57x and 6.19-115.25x (10.03-59.47x and 3.38-28.76x) faster than ARM Cortex CPU and Nvidia Jetson while only consuming 0.255W (0.809W), and is up to 1049.42x (133.84x) power-efficient than the workstation. P3Net improves the success rate by up to 28.2% and plans a near-optimal path, leading to a significantly better tradeoff between computation and solution quality than MPNet and the state-of-the-art sampling-based methods.

A Sequential Concept Drift Detection Method for On-Device Learning on Low-End Edge Devices

A practical issue of edge AI systems is that data distributions of trained dataset and deployed environment may differ due to noise and environmental changes over time. Such a phenomenon is known as a concept drift, and this gap degrades the performance of edge AI systems and may introduce system failures. To address this gap, a retraining of neural network models triggered by concept drift detection is a practical approach. However, since available compute resources are strictly limited in edge devices, in this paper we propose a lightweight concept drift detection method in cooperation with a recently proposed on-device learning technique of neural networks. In this case, both the neural network retraining and the proposed concept drift detection are done by sequential computation only to reduce computation cost and memory utilization. Evaluation results of the proposed approach shows that while the accuracy is decreased by 3.8%-4.3% compared to existing batch-based detection methods, it decreases the memory size by 88.9%-96.4% and the execution time by 1.3%-83.8%. As a result, the combination of the neural network retraining and the proposed concept drift detection method is demonstrated on Raspberry Pi Pico that has 264kB memory.

Communication Size Reduction of Federated Learning based on Neural ODE Model

Federated learning is a machine learning method in which data is not aggregated on a server, but is distributed to the edges, in consideration of security and privacy. ResNet is a classic but representative neural network that succeeds in deepening the neural network by learning a residual function that adds the inputs and outputs together. In federated learning, communication is performed between the server and edge devices to exchange weight parameters, but ResNet has deep layers and a large number of parameters, so communication size becomes large. In this paper, we use Neural ODE as a lightweight model of ResNet to reduce communication size in federated learning. In addition, we newly introduce a flexible federated learning using Neural ODE models with different number of iterations, which correspond to ResNet with different depths. The CIFAR-10 dataset is used in the evaluation, and the use of Neural ODE reduces communication size by approximately 90% compared to ResNet. We also show that the proposed flexible federated learning can merge models with different iteration counts.

An Efficient Accelerator for Deep Learning-based Point Cloud Registration on FPGAs

Point cloud registration is the basis for many robotic applications such as odometry and Simultaneous Localization And Mapping (SLAM), which are increasingly important for autonomous mobile robots. Computational resources and power budgets are limited on these robots, thereby motivating the development of resource-efficient registration method on low-cost FPGAs. In this paper, we propose a novel approach for FPGA-based 3D point cloud registration built upon a recent deep learning-based method, PointNetLK. A highly-efficient FPGA accelerator for PointNet-based feature extraction is designed and implemented on both low-cost and mid-range FPGAs (Avnet Ultra96v2 and Xilinx ZCU104). Our accelerator design is evaluated in terms of registration speed, accuracy, resource usage, and power consumption. Experimental results show that PointNetLK with our accelerator achieves up to 21.34x and 69.60x faster registration speed than the CPU counterpart and ICP, respectively, while only consuming 722mW and maintaining the same level of accuracy.

On-Device Learning: A Neural Network Based Field-Trainable Edge AI

In real-world edge AI applications, their accuracy is often affected by various environmental factors, such as noises, location/calibration of sensors, and time-related changes. This article introduces a neural network based on-device learning approach to address this issue without going deep. Our approach is quite different from de facto backpropagation based training but tailored for low-end edge devices. This article introduces its algorithm and implementation on a wireless sensor node consisting of Raspberry Pi Pico and low-power wireless module. Experiments using vibration patterns of rotating machines demonstrate that retraining by the on-device learning significantly improves an anomaly detection accuracy at a noisy environment while saving computation and communication costs for low power.