Machine Learning for Microcontroller-Class Hardware -- A Review

The advancements in machine learning opened a new opportunity to bring intelligence to the low-end Internet-of-Things nodes such as microcontrollers. Conventional machine learning deployment has high memory and compute footprint hindering their direct deployment on ultra resource-constrained microcontrollers. This paper highlights the unique requirements of enabling onboard machine learning for microcontroller class devices. Researchers use a specialized model development workflow for resource-limited applications to ensure the compute and latency budget is within the device limits while still maintaining the desired performance. We characterize a closed-loop widely applicable workflow of machine learning model development for microcontroller class devices and show that several classes of applications adopt a specific instance of it. We present both qualitative and numerical insights into different stages of model development by showcasing several use cases. Finally, we identify the open research challenges and unsolved questions demanding careful considerations moving forward.Comment: Accepted for publication at IEEE Sensors Journa

End-to-End Neural Network-based Speech Recognition for Mobile and Embedded Devices

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2020. 8. 성원용.Real-time automatic speech recognition (ASR) on mobile and embedded devices has been of great interest in recent years. Deep neural network-based automatic speech recognition demands a large number of computations, while the memory bandwidth and power storage of mobile devices are limited. The server-based implementation is often employed, but this increases latency or privacy concerns. Therefore, the need of the on-device ASR system is increasing. Recurrent neural networks (RNNs) are often used for the ASR model. The RNN implementation on embedded devices can suffer from excessive DRAM accesses, because the parameter size of a neural network usually exceeds that of the cache memory. Also, the parameters of RNN cannot be reused for multiple time-steps due to its feedback structure. To solve this problem, multi-time step parallelizable models are applied for speech recognition. The multi-time step parallelization approach computes multiple output samples at a time with the parameters fetched from the DRAM. Since the number of DRAM accesses can be reduced in proportion to the number of parallelization steps, a high processing speed can be achieved for the parallelizable model. In this thesis, a connectionist temporal classification (CTC) model is constructed by combining simple recurrent units (SRUs) and depth-wise 1-dimensional convolution layers for multi-time step parallelization. Both the character and word piece models are developed for the CTC model, and the corresponding RNN based language models are used for beam search decoding. A competitive WER for WSJ corpus is achieved using the entire model size of approximately 15MB. The system operates in real-time speed using only a single core ARM without GPU or special hardware. A low-latency on-device speech recognition system with a simple gated convolutional network (SGCN) is also proposed. The SGCN shows a competitive recognition accuracy even with 1M parameters. 8-bit quantization is applied to reduce the memory size and computation time. The proposed system features an online recognition with a 0.4s latency limit and operates in 0.2 RTF with only a single 900MHz CPU core. In addition, an attention-based model with the depthwise convolutional encoder is proposed. Convolutional encoders enable faster training and inference of attention models than recurrent neural network-based ones. However, convolutional models often require a very large receptive field to achieve high recognition accuracy, which not only increases the parameter size but also the computational cost and run-time memory footprint. A convolutional encoder with a short receptive field length often suffers from looping or skipping problems. We believe that this is due to the time-invariance of convolutions. We attempt to remedy this issue by adding positional information to the convolution-based encoder. It is shown that the word error rate (WER) of a convolutional encoder with a short receptive field size can be reduced significantly by augmenting it with positional information. Visualization results are presented to demonstrate the effectiveness of incorporating positional information. The streaming end-to-end ASR model is also developed by applying monotonic chunkwise attention.최근 모바일 및 임베디드 기기에서 실시간 동작하는 음성 인식 시스템을 개발하는 것이 큰 관심을 받고 있다. 깊은 인공 신경망 음성인식은 많은 양의 연산을 필요로 하는 반면, 모바일 기기의 메모리 대역폭이나 전력은 제한되어 있다. 이러한 한계 때문에 서버 기반 구현이 보통 사용되어지지만, 이는 지연 시간 및 사생활 침해 문제를 일으킨다. 따라서 모바일 기기 상 동작하는 음성 인식 시스템의 요구가 커지고 있다. 음성 인식 시스템에 주로 사용되는 모델은 재귀형 인공 신경망이다. 재귀형 인공 신경망의 모델 크기는 보통 캐시의 크기보다 크고 피드백 구조 때문에 재사용이 어렵기 때문에 많은 DRAM 접근을 필요로 한다. 이러한 문제를 해결하기 위해 다중 시간의 입력에대해 병렬화 가능한 모델을 이용한 음성 인식 시스템을 제안한다. 다중 시간 병렬화 기법은 한 번의 메모리 접근으로 여러 시간의 출력을 동시에 계산하는 방법이다. 병렬화 수에 따라 DRAM 접근 횟수를 줄일 수 있기 때문에, 병렬화 가능한 모델에 대하여 빠른 연산이 가능하다. 단순 재귀 유닛과 1차원 컨벌루션을 이용한 CTC 모델을 제시하였다. 문자와 단어 조각 수준의 모델이 개발되었다. 각 출력 단위에 해당하는 재귀형 신경망 기반 언어 모델을 이용하여 디코딩에 사용되었다. 전체 15MB의 메모리 크기로 WSJ 에서 높은 수준의 인식 성능을 얻었으며 GPU나 기타 하드웨어 없이 1개의 ARM CPU 코어로 실시간 처리를 달성하였다. 또한 단순 컨벌루션 인공 신경망 (SGCN)을 이용한 낮은 지연시간을 가지는 음성인식 시스템을 개발하였다. SGCN은 1M의 매우 낮은 변수 갯수로도 경쟁력 있는 인식 정확도를 보여준다. 추가적으로 8-bit 양자화를 적용하여 메모리 크기와 연산 시간을 감소 시켰다. 해당 시스템은 0.4초의 이론적 지연시간을 가지며 900MHz의 CPU 상에서 0.2의 RTF로 동작하였다. 추가적으로, 깊이별 컨벌루션 인코더를 이용한 어텐션 기반 모델이 개발되었다. 컨벌루션 기반의 인코더는 재귀형 인공 신경망 기반 모델보다 빠른 처리 속도를 가진다. 하지만 컨벌루션 모델은 높은 성능을 위해서 큰 입력 범위를 필요로 한다. 이는 모델 크기 및 연산량, 그리고 동작 시 메모리 소모를 증가 시킨다. 작은 크기의 입력 범위를 가지는 컨벌루션 인코더는 출력의 반복이나 생략으로 인하여 높은 오차율을 가진다. 이것은 컨벌루션의 시간 불변성 때문으로 여겨지며, 이 문제를 위치 인코딩 벡터를 이용하여 해결하였다. 위치 정보를 이용하여 작은 크기의 필터를 가지는 컨벌루션 모델의 성능을 높일 수 있음을 보였다. 또한 위치 정보가 가지는 영향을 시각화 하였다. 해당 방법을 단조 어텐션을 이용한 모델에 활용하여 컨벌루션 기반의 스트리밍 가능한 음성 인식 시스템을 개발하였다.1 Introduction 1 1.1 End-to-End Automatic Speech Recognition with Neural Networks . . 1 1.2 Challenges on On-device Implementation of Neural Network-based ASR 2 1.3 Parallelizable Neural Network Architecture 3 1.4 Scope of Dissertation 3 2 Simple Recurrent Units for CTC-based End-to-End Speech Recognition 6 2.1 Introduction 6 2.2 Related Works 8 2.3 Speech Recognition Algorithm 9 2.3.1 Acoustic modeling 10 2.3.2 Character-based model 12 2.3.3 Word piece-based model 14 2.3.4 Decoding 14 2.4 Experimental Results 15 2.4.1 Acoustic models 15 2.4.2 Word piece based speech recognition 22 2.4.3 Execution time analysis 25 2.5 Concluding Remarks 27 3 Low-Latency Lightweight Streaming Speech Recognition with 8-bit Quantized Depthwise Gated Convolutional Neural Networks 28 3.1 Introduction 28 3.2 Simple Gated Convolutional Networks 30 3.2.1 Model structure 30 3.2.2 Multi-time-step parallelization 31 3.3 Training CTC AM with SGCN 34 3.3.1 Regularization with symmetrical weight noise injection 34 3.3.2 8-bit quantization 34 3.4 Experimental Results 36 3.4.1 Experimental setting 36 3.4.2 Results on WSJ eval92 38 3.4.3 Implementation on the embedded system 38 3.5 Concluding Remarks 39 4 Effect of Adding Positional Information on Convolutional Neural Networks for End-to-End Speech Recognition 41 4.1 Introduction 41 4.2 Related Works 43 4.3 Model Description 45 4.4 Experimental Results 46 4.4.1 Effect of receptive field size 46 4.4.2 Visualization 49 4.4.3 Comparison with other models 53 4.5 Concluding Remarks 53 5 Convolution-based Attention Model with Positional Encoding for Streaming Speech Recognition 55 5.1 Introduction 55 5.2 Related Works 58 5.3 End-to-End Model for Speech Recognition 61 5.3.1 Model description 61 5.3.2 Monotonic chunkwise attention 62 5.3.3 Positional encoding 63 5.4 Experimental Results 64 5.4.1 Effect of positional encoding 66 5.4.2 Comparison with other models 68 5.4.3 Execution time analysis 70 5.5 Concluding Remarks 71 6 Conclusion 72 Abstract (In Korean) 86Docto

Game Theory Solutions in Sensor-Based Human Activity Recognition: A Review

The Human Activity Recognition (HAR) tasks automatically identify human activities using the sensor data, which has numerous applications in healthcare, sports, security, and human-computer interaction. Despite significant advances in HAR, critical challenges still exist. Game theory has emerged as a promising solution to address these challenges in machine learning problems including HAR. However, there is a lack of research work on applying game theory solutions to the HAR problems. This review paper explores the potential of game theory as a solution for HAR tasks, and bridges the gap between game theory and HAR research work by suggesting novel game-theoretic approaches for HAR problems. The contributions of this work include exploring how game theory can improve the accuracy and robustness of HAR models, investigating how game-theoretic concepts can optimize recognition algorithms, and discussing the game-theoretic approaches against the existing HAR methods. The objective is to provide insights into the potential of game theory as a solution for sensor-based HAR, and contribute to develop a more accurate and efficient recognition system in the future research directions

Edge Intelligence : Empowering Intelligence to the Edge of Network

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe

Edge Intelligence : Empowering Intelligence to the Edge of Network

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe

Fixed Point Analysis Workflow for efficient Design of Convolutional Neural Networks in Hearing Aids

Neural networks (NN) are a powerful tool to tackle complex problems in hearing aid research, but their use on hearing aid hardware is currently limited by memory and processing power. To enable the training with these constrains, a fixed point analysis and a memory friendly power of two quantization (replacing multiplications with shift operations) scheme has been implemented extending TensorFlow, a standard framework for training neural networks, and the Qkeras package [1, 2]. The implemented fixed point analysis detects quantization issues like overflows, underflows, precision problems and zero gradients. The analysis is done for each layer in every epoch for weights, biases and activations respectively. With this information the quantization can be optimized, e.g. by modifying the bit width, number of integer bits or the quantization scheme to a power of two quantization. To demonstrate the applicability of this method a case study has been conducted. Therefore a CNN has been trained to predict the Ideal Ratio Mask (IRM) for noise reduction in audio signals. The dataset consists of speech samples from the TIMIT dataset mixed with noise from the Urban Sound 8kand VAD-dataset at 0 dB SNR. The CNN was trained in floating point, fixed point and a power of two quantization. The CNN architecture consists of six convolutional layers followed by three dense layers. From initially 1.9 MB memory footprint for 468k float32 weights, the power of two quantized network is reduced to 236 kB, while the Short Term Objective Intelligibility (STOI) Improvement drops only from 0.074 to 0.067. Despite the quantization only a minimal drop in performance was observed, while saving up to 87.5 % of memory, thus being suited for employment in a hearing ai

An efficient human activity recognition model based on deep learning approaches

Human Activity Recognition (HAR) has gained traction in recent years in diverse areas such as observation, entertainment, teaching and healthcare, using wearable and smartphone sensors. Such environments and systems necessitate and subsume activity recognition, aimed at recognizing the actions, characteristics, and goals of one or more individuals from a temporal series of observations streamed from one or more sensors. Different developed models for HAR have been explained in literature. Deep learning systems and algorithms were shown to perform highly in HAR in recent years, but these algorithms need lots of computerization to be deployed efficiently in applications. This paper presents a HAR lightweight, low computing capacity, deep learning model, which is ideal for use in real-time applications. The generic HAR framework for smartphone sensor data is proposed, based on Long Short-Term Memory (LSTM) networks for time-series domains and standard Convolutional Neural Network (CNN) used for classification. The findings demonstrate that many of the deployed deep learning and machine learning techniques are surpassed by the proposed model. TRANSLATE with x English ArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersian // TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster Portal Back /