Choosing the Right Hardware for Embedded Machine Learning

1. Introduction

Machine learning is a branch of artificial intelligence that enables computers to learn from data and make predictions or decisions. Machine learning applications are becoming more and more popular in various domains, such as computer vision, natural language processing, speech recognition, and robotics.

However, most machine learning applications require a lot of computational power and memory, which are usually provided by cloud servers or high-performance computers. This poses some challenges, such as high latency, low reliability, high cost, and privacy issues. How can we overcome these challenges and bring machine learning closer to the edge devices, such as sensors, cameras, drones, or robots?

The answer is embedded machine learning, which is the process of running machine learning models on low-power and resource-constrained hardware platforms. Embedded machine learning can enable faster, cheaper, and more secure machine learning applications that can operate in real-time and offline.

But how do we choose the right hardware platform for embedded machine learning? What are the different types of hardware platforms that support embedded machine learning? What are the advantages and disadvantages of each type of hardware platform? How do we compare and trade-off between them?

In this blog post, we will explore these questions and provide you with some guidance on how to choose the right hardware platform for your embedded machine learning project. We will cover the following topics:

• What is embedded machine learning and why is it important?
• Hardware platforms for embedded machine learning, such as microcontrollers, single-board computers, FPGAs, and ASICs.
• Comparison and trade-offs of hardware platforms, such as performance, power consumption, cost, flexibility, and scalability.
• Conclusion and recommendations.

By the end of this blog post, you will have a better understanding of the different types of hardware platforms that support embedded machine learning and how to choose the best one for your needs. Let’s get started!

2. What is Embedded Machine Learning?

Embedded machine learning is the process of running machine learning models on low-power and resource-constrained hardware platforms, such as microcontrollers, single-board computers, FPGAs, and ASICs. These hardware platforms are often embedded in edge devices, such as sensors, cameras, drones, or robots, that perform tasks such as data collection, processing, and analysis.

Why is embedded machine learning important? There are several benefits of running machine learning models on the edge, such as:

• Low latency: Embedded machine learning can reduce the delay between data input and output, which is crucial for real-time applications, such as autonomous driving, face recognition, or gesture control.
• High reliability: Embedded machine learning can operate without depending on network connectivity or cloud availability, which can improve the robustness and resilience of the system, especially in harsh or remote environments.
• Low cost: Embedded machine learning can save the cost of data transmission and storage, as well as the cost of cloud services, which can make the system more affordable and scalable.
• High privacy: Embedded machine learning can protect the data privacy and security, as the data is processed locally and does not need to be sent to the cloud or a third party, which can prevent data leakage or misuse.

However, embedded machine learning also poses some challenges, such as:

• Limited resources: Embedded machine learning has to deal with the constraints of the hardware platforms, such as low memory, low processing power, low battery life, and low bandwidth, which can affect the performance and accuracy of the machine learning models.
• Complex trade-offs: Embedded machine learning has to balance between different factors, such as performance, power consumption, cost, flexibility, and scalability, which can require careful design and optimization of the hardware platforms and the machine learning models.

How can we overcome these challenges and choose the right hardware platform for embedded machine learning? In the next section, we will introduce the different types of hardware platforms that support embedded machine learning and discuss their features and limitations.

3. Hardware Platforms for Embedded Machine Learning

In this section, we will introduce the different types of hardware platforms that support embedded machine learning and discuss their features and limitations. We will focus on four main types of hardware platforms: microcontrollers, single-board computers, FPGAs, and ASICs. We will also provide some examples of each type of hardware platform and how they are used for embedded machine learning applications.

Microcontrollers are small and low-cost integrated circuits that contain a processor, memory, and input/output peripherals. They are often used to control simple devices, such as sensors, actuators, or LEDs. Microcontrollers are suitable for embedded machine learning applications that require low power consumption, small size, and low cost, such as wearable devices, smart home devices, or environmental monitoring devices. However, microcontrollers have limited memory and processing power, which can limit the complexity and accuracy of the machine learning models. Some examples of microcontrollers that support embedded machine learning are Arduino Nano 33 BLE Sense, ESP32, and STM32.

Single-board computers are small and inexpensive computers that contain a processor, memory, storage, and input/output ports on a single circuit board. They are often used to run operating systems, such as Linux or Windows, and execute various applications, such as web servers, media players, or gaming consoles. Single-board computers are suitable for embedded machine learning applications that require more processing power, memory, and storage than microcontrollers, such as face recognition, object detection, or speech recognition. However, single-board computers have higher power consumption, larger size, and higher cost than microcontrollers. Some examples of single-board computers that support embedded machine learning are Raspberry Pi, Jetson Nano, and BeagleBone.

Field-programmable gate arrays (FPGAs) are programmable integrated circuits that contain an array of logic blocks that can be configured to perform various functions, such as arithmetic, logic, or memory. They are often used to implement custom hardware designs, such as digital signal processing, encryption, or image processing. FPGAs are suitable for embedded machine learning applications that require high performance, low latency, and high flexibility, such as video analytics, medical imaging, or radar processing. However, FPGAs have high power consumption, high cost, and high complexity than microcontrollers and single-board computers. Some examples of FPGAs that support embedded machine learning are Intel Cyclone V, Xilinx Zynq, and Lattice ECP5.

Application-specific integrated circuits (ASICs) are custom-designed integrated circuits that perform a specific function, such as machine learning, graphics, or audio. They are often used to optimize the performance, power consumption, and cost of a particular application, such as smartphones, tablets, or gaming devices. ASICs are suitable for embedded machine learning applications that require the highest performance, lowest power consumption, and lowest cost, such as voice assistants, gesture control, or autonomous driving. However, ASICs have low flexibility, high design time, and high design cost than FPGAs, microcontrollers, and single-board computers. Some examples of ASICs that support embedded machine learning are Google Edge TPU, Apple Neural Engine, and Nvidia Tensor Cores.

3.1. Microcontrollers

Microcontrollers are one of the most common types of hardware platforms for embedded machine learning. They are small and low-cost integrated circuits that contain a processor, memory, and input/output peripherals. They are often used to control simple devices, such as sensors, actuators, or LEDs.

What are the benefits of using microcontrollers for embedded machine learning? Here are some of the main advantages:

• Low power consumption: Microcontrollers can run on batteries or solar power, which makes them ideal for applications that need to operate for a long time without recharging or plugging in.
• Small size: Microcontrollers can fit in tiny spaces, which makes them ideal for applications that need to be portable or wearable.
• Low cost: Microcontrollers are cheap and widely available, which makes them ideal for applications that need to be mass-produced or affordable.

However, microcontrollers also have some limitations that can affect the performance and accuracy of the machine learning models. Here are some of the main challenges:

• Limited memory: Microcontrollers have very little memory, which can limit the size and complexity of the machine learning models. For example, a typical microcontroller may have only 256 KB of flash memory and 32 KB of RAM, which is not enough to store a large neural network.
• Limited processing power: Microcontrollers have low processing power, which can limit the speed and efficiency of the machine learning models. For example, a typical microcontroller may have only a 32-bit processor running at 48 MHz, which is not enough to perform complex calculations or operations.

How can we overcome these limitations and run machine learning models on microcontrollers? There are some techniques and tools that can help us, such as:

• Model compression: Model compression is the process of reducing the size and complexity of the machine learning models, such as by pruning, quantization, or distillation. This can help us fit the models into the memory and improve the inference speed.
• Model optimization: Model optimization is the process of improving the performance and efficiency of the machine learning models, such as by using specialized libraries, frameworks, or compilers. This can help us leverage the hardware features and accelerate the computation.

Some examples of microcontrollers that support embedded machine learning are Arduino Nano 33 BLE Sense, ESP32, and STM32. These microcontrollers have some features that make them suitable for embedded machine learning, such as:

• Arduino Nano 33 BLE Sense: This microcontroller has a 64 MHz ARM Cortex-M4 processor, 1 MB of flash memory, and 256 KB of RAM. It also has a built-in Bluetooth Low Energy (BLE) module, a microphone, an accelerometer, a gyroscope, a magnetometer, a light sensor, a temperature sensor, a humidity sensor, and a color sensor. It can run machine learning models using the Arduino TensorFlow Lite library, which is a lightweight version of the TensorFlow framework.
• ESP32: This microcontroller has a 240 MHz dual-core Xtensa LX6 processor, 4 MB of flash memory, and 520 KB of RAM. It also has a built-in Wi-Fi and Bluetooth module, a touch sensor, a hall sensor, and an analog-to-digital converter. It can run machine learning models using the ESP32 TensorFlow Lite library, which is a port of the Arduino TensorFlow Lite library.
• STM32: This microcontroller has a 480 MHz ARM Cortex-M7 processor, 2 MB of flash memory, and 1 MB of RAM. It also has a built-in Ethernet and USB module, a digital-to-analog converter, a camera interface, and a cryptographic accelerator. It can run machine learning models using the STM32Cube.AI library, which is a tool that converts TensorFlow, Keras, or ONNX models into optimized code for the STM32 platform.

In summary, microcontrollers are a popular choice for embedded machine learning applications that require low power consumption, small size, and low cost. However, they also have limited memory and processing power, which can affect the performance and accuracy of the machine learning models. Therefore, we need to use model compression and optimization techniques and tools to run machine learning models on microcontrollers.

3.2. Single-Board Computers

Single-board computers (SBCs) are hardware platforms that consist of a single circuit board with all the components required for a functional computer, such as a processor, memory, storage, input/output ports, and network interfaces. SBCs are usually larger and more powerful than microcontrollers, but smaller and cheaper than desktop or laptop computers.

Some examples of popular SBCs are the Raspberry Pi, the Arduino Yun, the NVIDIA Jetson Nano, and the Google Coral Dev Board. These SBCs have different features and specifications, such as the processor type, the memory size, the storage capacity, the power consumption, and the supported operating systems and programming languages.

Why are SBCs suitable for embedded machine learning? There are several advantages of using SBCs for embedded machine learning, such as:

• High performance: SBCs can run complex machine learning models with high accuracy and speed, as they have more processing power and memory than microcontrollers. Some SBCs, such as the NVIDIA Jetson Nano and the Google Coral Dev Board, also have dedicated hardware accelerators for machine learning, such as GPUs or TPUs, which can boost the performance even further.
• High flexibility: SBCs can run various operating systems and programming languages, such as Linux, Windows, Python, C++, and Java, which can give more options and freedom for developing and deploying machine learning applications. SBCs can also connect to various peripherals and sensors, such as cameras, microphones, speakers, and displays, which can enable more interactive and diverse machine learning applications.
• High scalability: SBCs can communicate with other devices and networks, such as the Internet, Wi-Fi, Bluetooth, and Ethernet, which can enable more scalable and distributed machine learning applications. SBCs can also be integrated with cloud services, such as AWS, Azure, or Google Cloud, which can provide more storage and computing resources, as well as access to advanced machine learning frameworks and tools.

However, SBCs also have some limitations and challenges for embedded machine learning, such as:

• High power consumption: SBCs consume more power than microcontrollers, as they have more components and functionalities. This can limit the battery life and portability of the edge devices, as well as increase the heat generation and cooling requirements.
• High cost: SBCs are more expensive than microcontrollers, as they have more advanced and specialized hardware. This can increase the budget and complexity of the embedded machine learning project, especially if multiple SBCs are needed.

How can we choose the best SBC for our embedded machine learning project? In the next section, we will compare and trade-off between different hardware platforms, including SBCs, and provide some recommendations.

3.3. Field-Programmable Gate Arrays (FPGAs)

Field-programmable gate arrays (FPGAs) are hardware platforms that consist of an array of configurable logic blocks (CLBs) that can be programmed to perform various functions, such as arithmetic, logic, memory, or communication. FPGAs are different from microcontrollers and single-board computers, as they do not have a fixed processor or instruction set, but rather can be customized to implement any desired circuit or algorithm.

Some examples of popular FPGAs are the Xilinx FPGAs, the Intel FPGAs, and the Lattice FPGAs. These FPGAs have different features and specifications, such as the number of CLBs, the clock speed, the power consumption, and the supported programming languages and tools.

Why are FPGAs suitable for embedded machine learning? There are several advantages of using FPGAs for embedded machine learning, such as:

• High efficiency: FPGAs can run machine learning models with high efficiency and low latency, as they can parallelize and optimize the computation and data flow, as well as eliminate the overhead of instruction fetching and decoding. FPGAs can also adapt to different machine learning models and tasks, as they can be reprogrammed and reconfigured on the fly.
• Low power consumption: FPGAs can consume less power than microcontrollers and single-board computers, as they can reduce the switching activity and the number of transistors. FPGAs can also dynamically adjust the power consumption according to the workload and the performance requirements.
• High security: FPGAs can protect the machine learning models and data from unauthorized access or modification, as they can encrypt and authenticate the communication and the configuration. FPGAs can also prevent reverse engineering or tampering, as they can lock and erase the configuration.

However, FPGAs also have some limitations and challenges for embedded machine learning, such as:

• High cost: FPGAs are more expensive than microcontrollers and single-board computers, as they have more complex and specialized hardware. This can increase the budget and complexity of the embedded machine learning project, especially if multiple FPGAs are needed.
• High difficulty: FPGAs are more difficult to program and debug than microcontrollers and single-board computers, as they require more expertise and knowledge in hardware design and verification. FPGAs also have less support and documentation than other hardware platforms, which can make the development and deployment process more challenging.

How can we choose the best FPGA for our embedded machine learning project? In the next section, we will compare and trade-off between different hardware platforms, including FPGAs, and provide some recommendations.

3.4. Application-Specific Integrated Circuits (ASICs)

Application-specific integrated circuits (ASICs) are hardware platforms that are designed and optimized for a specific purpose or application. ASICs are usually the most powerful and efficient hardware platforms for embedded machine learning, as they can achieve high performance, low power consumption, and low cost.

ASICs are custom-made chips that implement the machine learning model and the associated logic in hardware. This means that the machine learning model is hardwired into the chip and cannot be changed or updated. ASICs can also integrate other components, such as memory, sensors, or communication modules, to form a complete system on a chip (SoC).

Some examples of ASICs for embedded machine learning are:

• Google’s Tensor Processing Unit (TPU): A specialized chip that accelerates the execution of machine learning models based on the TensorFlow framework. TPUs are used in Google’s cloud services and edge devices, such as Google Home and Pixel phones.
• Apple’s Neural Engine: A dedicated hardware unit that performs neural network operations on Apple’s devices, such as iPhones, iPads, and Macs. The Neural Engine supports various machine learning tasks, such as face recognition, natural language processing, and image processing.
• Intel’s Movidius Myriad: A low-power chip that enables machine vision and deep learning applications on edge devices, such as drones, cameras, and robots. The Myriad chip can run multiple machine learning models simultaneously and support various frameworks, such as TensorFlow, Caffe, and OpenVINO.

ASICs have several advantages for embedded machine learning, such as:

• High performance: ASICs can achieve the highest performance among the hardware platforms, as they can exploit the parallelism and optimization of the machine learning model and the hardware architecture. ASICs can also run at high frequencies and process large amounts of data.
• Low power consumption: ASICs can consume the lowest power among the hardware platforms, as they can eliminate the overhead and inefficiency of general-purpose processors and memory. ASICs can also use low-voltage and low-current circuits and implement power management techniques.
• Low cost: ASICs can reduce the cost of the hardware platform, as they can integrate all the components and functions into a single chip. ASICs can also use standard fabrication processes and materials and benefit from economies of scale.

However, ASICs also have some limitations for embedded machine learning, such as:

• Low flexibility: ASICs are the least flexible among the hardware platforms, as they cannot be reprogrammed or updated once they are fabricated. ASICs are also tied to a specific machine learning model and framework and cannot support new or different models or frameworks.
• High design complexity: ASICs require the most complex and time-consuming design process among the hardware platforms, as they involve multiple steps, such as specification, simulation, verification, synthesis, layout, and testing. ASICs also require specialized tools and skills and collaboration between hardware and software engineers.
• High risk: ASICs entail the highest risk among the hardware platforms, as they have high upfront costs and long development cycles. ASICs also face the challenges of changing requirements, technological obsolescence, and market competition.

ASICs are the most suitable hardware platforms for embedded machine learning applications that require high performance, low power consumption, and low cost, and that have stable and well-defined machine learning models and frameworks. However, ASICs are also the most challenging hardware platforms to design, develop, and deploy, and that have low flexibility and high risk.

4. Comparison and Trade-offs of Hardware Platforms

In the previous section, we introduced the different types of hardware platforms that support embedded machine learning, such as microcontrollers, single-board computers, FPGAs, and ASICs. In this section, we will compare and trade-off between these hardware platforms and help you choose the best one for your embedded machine learning project.

There is no single best hardware platform for embedded machine learning, as each platform has its own strengths and weaknesses, and the choice depends on various factors, such as the machine learning model, the application, the environment, and the budget. However, we can use some criteria to evaluate and compare the hardware platforms, such as:

• Performance: How fast and accurate can the hardware platform run the machine learning model? How much data can the hardware platform process and store? How well can the hardware platform handle complex and dynamic machine learning tasks?
• Power consumption: How much energy does the hardware platform consume when running the machine learning model? How long can the hardware platform operate on battery or solar power? How efficient is the hardware platform in terms of performance per watt?
• Cost: How much does the hardware platform cost to purchase and maintain? How easy is the hardware platform to obtain and deploy? How scalable is the hardware platform for large-scale or distributed applications?
• Flexibility: How adaptable is the hardware platform to different machine learning models and frameworks? How easy is the hardware platform to program and update? How compatible is the hardware platform with other devices and systems?

Based on these criteria, we can summarize the comparison and trade-offs of the hardware platforms as follows:

As you can see, there is a trade-off between performance, power consumption, cost, and flexibility among the hardware platforms. For example, ASICs have the highest performance and the lowest power consumption and cost, but they also have the lowest flexibility and the highest design complexity. On the other hand, microcontrollers have the lowest performance and the highest flexibility, but they also have the lowest power consumption and cost.

Therefore, the best hardware platform for your embedded machine learning project depends on your specific requirements and constraints. You need to consider the following questions:

• What is the machine learning model that you want to run on the hardware platform? How complex and dynamic is it? How often does it need to be updated or changed?
• What is the application that you want to implement with the hardware platform? How critical and time-sensitive is it? How much data does it need to process and store?
• What is the environment that you want to deploy the hardware platform? How harsh or remote is it? How reliable and secure is the network connectivity?
• What is the budget that you have for the hardware platform? How much can you afford to spend on the hardware platform and its maintenance? How scalable is your application and how many hardware platforms do you need?

By answering these questions, you can narrow down your choices and select the most suitable hardware platform for your embedded machine learning project. In the next section, we will conclude this blog post and provide you with some recommendations and resources to help you get started with embedded machine learning.

5. Conclusion

In this blog post, we have explored the different types of hardware platforms that support embedded machine learning, such as microcontrollers, single-board computers, FPGAs, and ASICs. We have also compared and trade-off between these hardware platforms and provided some guidance on how to choose the best one for your embedded machine learning project.

We have learned that there is no single best hardware platform for embedded machine learning, as each platform has its own strengths and weaknesses, and the choice depends on various factors, such as the machine learning model, the application, the environment, and the budget. However, we have also learned that we can use some criteria to evaluate and compare the hardware platforms, such as performance, power consumption, cost, and flexibility.

We have seen that ASICs are the most powerful and efficient hardware platforms for embedded machine learning, but they also have the lowest flexibility and the highest design complexity. On the other hand, microcontrollers are the most flexible and easy-to-use hardware platforms for embedded machine learning, but they also have the lowest performance and the highest resource constraints. Single-board computers and FPGAs are somewhere in between, offering a balance between performance, power consumption, cost, and flexibility, but also requiring more skills and tools to program and optimize.

Therefore, the best hardware platform for your embedded machine learning project depends on your specific requirements and constraints. You need to consider the following questions:

• What is the machine learning model that you want to run on the hardware platform? How complex and dynamic is it? How often does it need to be updated or changed?
• What is the application that you want to implement with the hardware platform? How critical and time-sensitive is it? How much data does it need to process and store?
• What is the environment that you want to deploy the hardware platform? How harsh or remote is it? How reliable and secure is the network connectivity?
• What is the budget that you have for the hardware platform? How much can you afford to spend on the hardware platform and its maintenance? How scalable is your application and how many hardware platforms do you need?

By answering these questions, you can narrow down your choices and select the most suitable hardware platform for your embedded machine learning project. We hope that this blog post has been helpful and informative for you and that you have gained some insights and tips on how to choose the right hardware platform for embedded machine learning.

If you want to learn more about embedded machine learning and the hardware platforms that support it, here are some resources that you can check out:

• TensorFlow Lite: A framework for running machine learning models on microcontrollers and single-board computers.
• Getting started with Google Coral’s TPU USB Accelerator: A tutorial on how to use Google’s TPU to accelerate machine learning applications on single-board computers.
• Xilinx AI and Machine Learning: A platform for developing and deploying machine learning applications on FPGAs.
• An in-depth look at Google’s first Tensor Processing Unit (TPU): A blog post that explains the design and performance of Google’s ASIC for machine learning.

Thank you for reading this blog post and we hope that you have enjoyed it. If you have any questions or feedback, please feel free to leave a comment below. We would love to hear from you and help you with your embedded machine learning project. Happy learning!