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The main function of the battery connection cable are to transmit current and electrical energy. They direct the current from the positive end of the battery through the positive connector, and then transmits it to the electronic device through the cable. At the same time, the negative connector directs the current from the electronic device back to the negative end of the battery, forming a circuit closure.
Battery connection cables are usually of a certain length and flexibility in order to provide a flexible connection between the electronic device and the battery. It may also have a protective housing to protect the cable from damage or wear.
When choosing a battery connection cable, you need to consider factors such as current transmission requirements, battery and device connector types, and cable length and material. Proper selection and use of battery connection cables can ensure a reliable connection and power transfer between the battery and the device.
High precision keyword speech recognition based on Cortex-M processor
In this paper, we explore how to optimize neural network architectures to fit within the memory and computational constraints of microcontrollers without sacrificing accuracy. Specifically, we focus on deep separable convolutional neural networks (DS-CNNs) for keyword recognition on Arm Cortex-M processors, demonstrating their potential in resource-constrained environments.
Keyword spotting (KWS) is a crucial component for voice-enabled interactions in smart devices, requiring both real-time performance and high precision to deliver an optimal user experience. Neural networks have become a preferred choice for KWS due to their superior accuracy compared to traditional speech processing techniques.
The power consumption of KWS applications is a major concern, as they must remain "always on." While these applications can run on dedicated DSPs or high-performance CPUs, they are better suited for Arm Cortex-M microcontrollers, which are cost-effective and often used at the edge of the Internet of Things (IoT).
Deploying a neural network-based KWS system on a Cortex-M microcontroller presents several challenges, including limited memory space and computing resources. A typical Cortex-M system offers only a few hundred kilobytes of available memory, requiring the entire model—weights, activations, and input/output—to fit within this small footprint. Additionally, real-time operation imposes strict limits on the number of operations per inference.
Common KWS architectures include Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Convolutional Recurrent Neural Networks (CRNNs), and Deep Separable Convolutional Neural Networks (DS-CNNs). Among these, DS-CNNs offer a compelling balance of efficiency and performance by reducing the number of parameters through depth-wise separable convolutions, making them ideal for microcontroller deployment.
When designing a KWS system for Cortex-M, memory usage and execution time are critical factors. The following constraints represent typical configurations for small, medium, and large Cortex-M systems.
We conducted a hyperparameter search to fine-tune the model, optimizing architecture and parameters to stay within the microcontroller’s limits. The results show that DS-CNNs achieve the highest accuracy with significantly lower memory and computational demands.
The KWS application was deployed on a Cortex-M7-based STM32F746G-DISCO development board, using an 8-bit DNN model. It performs 10 inferences per second, with each inference taking approximately 12 milliseconds. The system uses around 70 KB of memory, with 66 KB allocated for weights, 1 KB for activations, and 2 KB for audio I/O and MFCC features. The microcontroller can enter a low-power WFI mode when idle, further conserving energy.
Overall, the Arm Cortex-M processor enables high-accuracy keyword recognition while maintaining low memory and computational requirements. The DS-CNN architecture stands out for its efficiency and performance in this context.
For developers interested in exploring this technology, code, model definitions, and pre-trained models are available on GitHub. Additionally, our new machine learning developer site offers a comprehensive resource library, product details, and tutorials to support edge AI development.