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How to interface an I2S microphone with Beaglebone Black(BBB)

10 minutes, 23 seconds read

 I have been writing a large variety of computer programs since a long time, but there was this question, the answer to which was elusive for a long time.

  • How are they converted to binary data, and how is that interpreted by my computer?
  • How do we create devices, and how do they work?

My fascination started with a smart wall clock (http://ingrein.com) that I thought was a very cool gadget to have at home. I wanted to build something like that on my own, but didn’t had know how. So I started on a journey to learn embedded systems and their functioning.

I moved from Arduino to Raspberry Pi, and then to RedBear Duo, learning new things at every step. And then finally came BeagleBone Black. I had always wondered how Linux kernel works, is it something that I can compile on my own, and execute? I have been trying to solve this problem for so long and I want to thank Pavel Botev for helping me out on this.

BeagleBone Black (BBB) comes with a TI processor AM3358. So in order to build Linux kernel for this board, you will need TI SDK that can be downloaded at http://www.ti.com/tool/PROCESSOR-SDK-AM335X.

You will need to download and install the binary (Linux Processor SDK for AM335x) from the link above. Help on installation is available here — http://software-dl.ti.com/processor-sdk-linux/esd/docs/latest/linux/index.html.

There are two distinct steps in the installation of SDK. First setting the execute permission on the SDK bin file, and second to execute it.

$ chmod +x ./ti-processor-sdk-linux-[platformName]-evm-xx.xx.xx.xx-Linux-x86-Install.bin
$ ./ti-processor-sdk-linux-[platformName]-evm-xx.xx.xx.xx-Linux-x86-Install.bin

Once the TI Processor SDK is installed, you will find the following file structure in the install location.

 

This SDK contains both the Linux kernel, and the Root File System, and other cross compile binaries (compiler) to compile the kernel. Assuming ti-processor-sdk-home is the SDK install location, you will find the kernel files at

<ti-processor-sdk-home>/board-support/linux-4.9.69+gitAUTOINC+xxxx (The exact version may vary depending on the version of the processor SDK)

and the RFS at

<ti-processor-sdk-home>/filesystem

You can copy these to separate folders so you always have the original SDK copy. In case anything goes wrong, and you want to restart from beginning, you have the kernel, and RFS that you can copy again from the Processor SDK.

Lets assume you copied the kernel files to location ~/linux-4.9.69, and changed your current directory to where you copied the kernel.

$ cd ~/linux-4.9.69

Before you compile the kernel, we must prepare the kernel by telling what is the board that we want to compile the kernel for? In other words you define the configuration by selecting appropriate defconfig file. For BeagleBone Black, we need to use “tisdk_am335x-evm_defconfig”. All config files are present in arch/arm/configs folder.

Command for setting this configuration is

$ make ARCH=arm CROSS_COMPILE=<ti-processor-sdk-home>/linux-devkit/sysroots/x86_64-arago-linux/usr/bin/arm-linux-gnueabihf- tisdk_am335x-evm_defconfig

Please note the space between “arm-linux-gnueabihf-” and “tisdk_am335x-evm_defconfig” in the above command.

You may want to configure your linux distribution further by informing the compiler what all files/modules should be included for compilation. “menuconfig” is the target for this configuration, and the full command to run menuconfig is below.

But before you run menuconfig target, there is one more step. We need to tell menuconfig what all options should be shown in menuconfig, and how. Though most of the settings are good by default, we need to do one change in the kernel

$ vi ti-processor-sdk-home/board-support/linux-4.9.69+gitAUTOINC+xxxx/sound/soc/codecs/Kconfig

Find line

config SND_SOC_PCM5102A
       tristate

And replace it with

config SND_SOC_PCM5102A
       tristate "Texas Instruments PCM5102a Dummy Codec Driver"

The above line “Texas Instruments PCM5102a Dummy Codec Driver” helps you identify the codec in the menuconfig stage.

Finally run “menuconfig” target with the following command.

$ make ARCH=arm CROSS_COMPILE=<ti-processor-sdk-home>/linux-devkit/sysroots/x86_64-arago-linux/usr/bin/arm-linux-gnueabihf- menuconfig

Please note again that menuconfig is the target name, and the value for CROSS_COMPILE flag ends with a hyphen as “arm-linux-gnueabihf-”. There should be space between “arm-linux-gnueabihf-” and “menuconfig”.

Running “menuconfig” target opens up a menu through which you can select which modules you would like to be compiled in-line, i.e. along with rest of kernel code, and which ones to be compiled, and included as modules. Mark module PCM5102a to be inline compiled along with other kernel files.

Now in order to compile the Linux Kernel, you have the kernel source files, and the cross compile binaries needed to compile the source. Compile the kernel using

$ cd ~/linux-4.9.69
$ make ARCH=arm CROSS_COMPILE=<ti-processor-sdk-home>/linux-devkit/sysroots/x86_64-arago-linux/usr/bin/arm-linux-gnueabihf- uImage LOADADDR=0x80008000 -j4

The above command compiles the kernel and keeps the image at arch/arm/boot/uImage. You can copy this image and flash it to the board, or transfer it via tftp. I shall explain the process of using tftp later.

The device tree source files are present in linux-4.9.69/arch/arm/boot/dts folder in the kernel. The device tree is the code that tells the kernel what all hardware is available on the board, and how is it configured.

Before we compile the device tree, we need to know which device tree we will be using. As this experiment is about BBB, it is obvious that BeagleBone’s device tree should be used. It is present as linux-4.9.69/arch/arm/boot/dts/am335x-boneblack.dts.

But we want to interface an I2S mems microphone (SPH0645LM4H, https://www.adafruit.com/product/3421) with BBB, we will need to tell the device tree of its presence, and its configuration. We will include all microphone related configuration in a separate DTSI file (include file, which can be included in the parent device tree source).

$ vi am335x-boneblack-pcm5102a.dtsi

The content of this include file is as below

/*
* Copyright(C) 2016 Texas Instruments Incorporated- http://www.ti.com/
*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License version 2 as
* published by the Free Software Foundation.
*/
&am33xx_pinmux {
 mcasp1_pins: mcasp1_pins{
  pinctrl-single,pins = <
   /* sink must enable receivers */
   AM33XX_IOPAD(0x9a0, PIN_INPUT_PULLDOWN | MUX_MODE3) /* P9_42 mcasp1_aclkx - bit clock */
   AM33XX_IOPAD(0x9a4, PIN_INPUT_PULLDOWN | MUX_MODE3) /* P9_27 mcasp1_fsx - frame sync */
   AM33XX_IOPAD(0x9a8, PIN_INPUT_PULLDOWN | MUX_MODE3) /* P9_41 mcasp1_axr0 - i2s input */
  >;
 };
};
&mcasp1 {
 #sound-dai-cells = <0>;
 pinctrl-names = "default";
 pinctrl-0 = <&mcasp1_pins>;
 status = "okay";
 op-mode = <0>; /* MCASP_IIS_MODE */
 tdm-slots = <2>;
 num-serializer = <4>;
 serial-dir = < /* 1 TX 2 RX 0 unused */
  2 1 0 0
 >;
 rx-num-evt = <32>;
 tx-num-evt = <32>;
};
/ {
 pcm5102a: pcm5102a {
  #sound-dai-cells = <0>;
  compatible = "ti,pcm5102a";
  status = "okay";
 };
clk_mcasp1_fixed: clk_mcasp1_fixed {
  #clock-cells = <0>;
  compatible = "fixed-clock";
  clock-frequency = <24576000>;
 };
clk_mcasp1: clk_mcasp1 {
  #clock-cells = <0>;
  compatible = "gpio-gate-clock";
  clocks = <&clk_mcasp1_fixed>;
  enable-gpios = <&gpio1 27 0>; /* BeagleBone Black Clk enable on GPIO1_27 */
 };
sound1:sound@1 {
  compatible = "simple-audio-card";
  simple-audio-card,name = "PCM5102a";
  simple-audio-card,format = "i2s";
  simple-audio-card,bitclock-master = <&sound1_master>;
  simple-audio-card,frame-master = <&sound1_master>;
  simple-audio-card,bitclock-inversion;
sound1_master: simple-audio-card,cpu {
   sound-dai = <&mcasp1>;
   system-clock-direction = "out";
   system-clock-frequency = <24576000>;
   clocks = <&clk_mcasp1>;
  };
  
  simple-audio-card,codec{
   sound-dai = <&pcm5102a>;
   #sound-dai-cells = <0>;
  };
 };
};

Now we need to include this “am335x-boneblack-pcm5102a.dtsi” file in “am335x-boneblack.dts”. Just add this line at the end of “am335x-boneblack.dts”.

#include "am335x-boneblack-pcm5102a.dtsi"

The device tree can be compiled using

$ cd ~/linux-4.9.69 
$ make ARCH=arm CROSS_COMPILE=<ti-processor-sdk-home>/linux-devkit/sysroots/x86_64-arago-linux/usr/bin/arm-linux-gnueabihf- dtbs

The above command will result in a device tree binary within arch/arm/boot/dts/ folder. The file is named am335x-boneblack.dtb

Lets now talk about how the MEMS microphone should be wired up. We can focus only on the BeagleBone column of the image below.

 
 

Booting the BBB

Now that all configuration is setup, we should march ahead with booting of your BBB. But wait, what you have is a kernel image (uImage) and a device tree binary (am335x-boneblack.dtb). But how do we send these to our BBB?

Instead of flashing the kernel, device tree, and the RFS to an SD card, and then putting the SD card to BBB, we will makes these available to BBB directly from the host computer via TFTP (for uImage, & DTB) and NFS (for RFS).

TFTP

We will use TFTP to provide the kernel image, and DTB to the BBB. Go ahead and install TFTP on your host computer.

sudo apt-get install tftpd-hpa

Now let us configure TFTP and tell it the location of the files we need to transfer to the BBB. TFTP configuration files is present as/etc/default/tftpd-hpa. Example configuration is below

# /etc/default/tftpd-hpa
TFTP_USERNAME="tftp"
TFTP_DIRECTORY="/home/parag/linux-4.9.69/arch/arm/boot"
TFTP_ADDRESS=":69"
TFTP_OPTIONS="--secure --create"

The above configuration makes “/home/parag/linux-4.9.69/arch/arm/boot” as TFTP_DIRECTORY, which is the default directory where tftp looks for files that it can transfer. TFTP is not known to work very well with nested directories, so we must ensure that both files (uImage, and DTB) are available in this directory.

As uImage is created in above directory itself, so its not a problem, and TFTP can easily transfer it. However DTB is formed within boot/dts. We can create a symbolic link in the boot itself, and make it point to DTB file present in dts directory to make it work.

ln -s dts/am335x-boneblack.dtb am335x-boneblack.dtb

Sharing RFS (Root File System) over NFS (Network File System)

RFS or the Root File System contains binaries that you typically see in any linux distribution. RFS is made available by TI SDK as indicated early in this article. You can just copy those files from SDK, and keep it at a desired location from where you can share them over network via NFS.

NFS server can be installed on ubuntu host computer with the following commands

sudo apt-get update
sudo apt-get install nfs-kernel-server

Once NFS is installed, you can proceed with its configuration. Edit /etc/exports

sudo vim /etc/exports

You can configure the above file with the following setting

/home/parag/bbone/rootfs        *(rw,sync,no_root_squash,no_subtree_check)

Note, I have kept my RFS files in /home/parag/bbone/rootfs. You change this setting depending upon where you have copied the RFS files to.

Finally, booting the BBB!!

After all this hard work, its time to see the magic. Connect BBB with LAN cable, and connect it to the same network as your host computer.

Power up the BBB. Assuming you have minicom or any other serial monitor set up, you should be able to see the uboot logs. Immediately press space key so the bootloader (uboot) does not boot the kernel available in BBB, but stops for further commands. Type commands as below to help BBB connect to the network.

>setenv autoload no
>setenv serverip 192.168.1.101 
>setenv gatewayip 192.168.1.1
>dhcp

I have used 192.168.1.101 as IP of my host computer, and 192.168.1.1 as the gateway. You will need to choose these according to your setup. Finally dhcp command will help BBB to be allocated an IP address from your router.

If everything goes on file, you will see output from BBB uboot that an IP has been assigned. Next command as follows

>tftpboot 0x80F80000 am335x-boneblack.dtb && tftpboot 0x80007FC0 uImage

The above command instructs u-boot to download the device tree image from the serverip instructed earlier, and copy the same to address 0x80F80000 in RAM. Kernel uImage is also downloaded from the host serverip and copied to 0x80007FC0.

Boot, finally!!

The last two commands to start the boot process are as below

>setenv bootargs console=ttyO0,115200n8 noinitrd rw ip=dhcp root=/dev/nfs nfsroot=192.168.1.101:/home/parag/bbone/rootfs nfsrootdebug earlyprintk
>bootm 0x80007FC0 - 0x80F80000

The first command above sets up the bootargs. Change the setting as per your environment. The last command starts the boot process.

Soon you should see the kernel boot to complete, and a login prompt to appear. Login using root as user. No password should be needed.

Unexpected Signal on P9_41 :(

Now you will find (on your oscilloscope) that the moment you boot the kernel, you start getting a signal (square wave) on the data pin (P9–41). Ideally there should be no signal on the data pin till you start recording using the “arecord” command.

Here is the link to get the zoom version for this image below.

You would notice there is pinmux settings for clkout2 (mode 3) for Pin P9_41A which is the data pin. We need to disable this setting so that data pin receives only the data we record from microphone, and not from any other source.

The above observation is because of a configuration in the am335x-bone-common.dtsi (a file included in am335x-boneblack.dts).

&am335x_pinmux{
pinctrl-names = "default"
pinctrl-0 = <&clkout2_pins>

It is this line `pinctrl-0 = <&clkout2_pins>` that causes the signals on data pin. We need to comment this out like below.

&am335x_pinmux{
pinctrl-names = "default"
/*pinctrl-0 = <&clkout2_pins>*/

After this above change, we need to build again the device tree, and reboot the kernel. The data pin should not have any signal now till we start recording with the command.

$ arecord -Dhw:1,0 -f S32_LE -t wav -c 1 -d 60 -vvv /tmp/audio.wav

The above command shall start recording mono sound (single channel) at /tmp/audio.wav. The above command’s -D flag (-Dhw:1,0) assumes your PCM5102a sound card is listed at index 1. This index can be confirmed by listing down all cards and seeing the output of the command below

$ arecord -l

If you found this article helpful, let me know in the comment section below.


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AI Code Assistants: Revolution Unveiled

AI code assistants are revolutionizing software development, with Gartner predicting that 75% of enterprise software engineers will use these tools by 2028, up from less than 10% in early 2023. This rapid adoption reflects the potential of AI to enhance coding efficiency and productivity, but also raises important questions about the maturity, benefits, and challenges of these emerging technologies.

Code Assistance Evolution

The evolution of code assistance has been rapid and transformative, progressing from simple autocomplete features to sophisticated AI-powered tools. GitHub Copilot, launched in 2021, marked a significant milestone by leveraging OpenAI’s Codex to generate entire code snippets 1. Amazon Q, introduced in 2023, further advanced the field with its deep integration into AWS services and impressive code acceptance rates of up to 50%. GPT (Generative Pre-trained Transformer) models have been instrumental in this evolution, with GPT-3 and its successors enabling more context-aware and nuanced code suggestions.

Image Source

  • Adoption rates: By 2023, over 40% of developers reported using AI code assistants.
  • Productivity gains: Tools like Amazon Q have demonstrated up to 80% acceleration in coding tasks.
  • Language support: Modern AI assistants support dozens of programming languages, with GitHub Copilot covering over 20 languages and frameworks.
  • Error reduction: AI-powered code assistants have shown potential to reduce bugs by up to 30% in some studies.

These advancements have not only increased coding efficiency but also democratized software development, making it more accessible to novice programmers and non-professionals alike.

Current Adoption and Maturity: Metrics Defining the Landscape

The landscape of AI code assistants is rapidly evolving, with adoption rates and performance metrics showcasing their growing maturity. Here’s a tabular comparison of some popular AI coding tools, including Amazon Q:

Amazon Q stands out with its specialized capabilities for software developers and deep integration with AWS services. It offers a range of features designed to streamline development processes:

  • Highest reported code acceptance rates: Up to 50% for multi-line code suggestions
  • Built-in security: Secure and private by design, with robust data security measures
  • Extensive connectivity: Over 50 built-in, managed, and secure data connectors
  • Task automation: Amazon Q Apps allow users to create generative AI-powered apps for streamlining tasks

The tool’s impact is evident in its adoption and performance metrics. For instance, Amazon Q has helped save over 450,000 hours from manual technical investigations. Its integration with CloudWatch provides valuable insights into developer usage patterns and areas for improvement.

As these AI assistants continue to mature, they are increasingly becoming integral to modern software development workflows. However, it’s important to note that while these tools offer significant benefits, they should be used judiciously, with developers maintaining a critical eye on the generated code and understanding its implications for overall project architecture and security.

AI-Powered Collaborative Coding: Enhancing Team Productivity

AI code assistants are revolutionizing collaborative coding practices, offering real-time suggestions, conflict resolution, and personalized assistance to development teams. These tools integrate seamlessly with popular IDEs and version control systems, facilitating smoother teamwork and code quality improvements.

Key features of AI-enhanced collaborative coding:

  • Real-time code suggestions and auto-completion across team members
  • Automated conflict detection and resolution in merge requests
  • Personalized coding assistance based on individual developer styles
  • AI-driven code reviews and quality checks

Benefits for development teams:

  • Increased productivity: Teams report up to 30-50% faster code completion
  • Improved code consistency: AI ensures adherence to team coding standards
  • Reduced onboarding time: New team members can quickly adapt to project codebases
  • Enhanced knowledge sharing: AI suggestions expose developers to diverse coding patterns

While AI code assistants offer significant advantages, it’s crucial to maintain a balance between AI assistance and human expertise. Teams should establish guidelines for AI tool usage to ensure code quality, security, and maintainability.

Emerging trends in AI-powered collaborative coding:

  • Integration of natural language processing for code explanations and documentation
  • Advanced code refactoring suggestions based on team-wide code patterns
  • AI-assisted pair programming and mob programming sessions
  • Predictive analytics for project timelines and resource allocation

As AI continues to evolve, collaborative coding tools are expected to become more sophisticated, further streamlining team workflows and fostering innovation in software development practices.

Benefits and Risks Analyzed

AI code assistants offer significant benefits but also present notable challenges. Here’s an overview of the advantages driving adoption and the critical downsides:

Core Advantages Driving Adoption:

  1. Enhanced Productivity: AI coding tools can boost developer productivity by 30-50%1. Google AI researchers estimate that these tools could save developers up to 30% of their coding time.
IndustryPotential Annual Value
Banking$200 billion – $340 billion
Retail and CPG$400 billion – $660 billion
  1. Economic Impact: Generative AI, including code assistants, could potentially add $2.6 trillion to $4.4 trillion annually to the global economy across various use cases. In the software engineering sector alone, this technology could deliver substantial value.
  1. Democratization of Software Development: AI assistants enable individuals with less coding experience to build complex applications, potentially broadening the talent pool and fostering innovation.
  2. Instant Coding Support: AI provides real-time suggestions and generates code snippets, aiding developers in their coding journey.

Critical Downsides and Risks:

  1. Cognitive and Skill-Related Concerns:
    • Over-reliance on AI tools may lead to skill atrophy, especially for junior developers.
    • There’s a risk of developers losing the ability to write or deeply understand code independently.
  2. Technical and Ethical Limitations:
    • Quality of Results: AI-generated code may contain hidden issues, leading to bugs or security vulnerabilities.
    • Security Risks: AI tools might introduce insecure libraries or out-of-date dependencies.
    • Ethical Concerns: AI algorithms lack accountability for errors and may reinforce harmful stereotypes or promote misinformation.
  3. Copyright and Licensing Issues:
    • AI tools heavily rely on open-source code, which may lead to unintentional use of copyrighted material or introduction of insecure libraries.
  4. Limited Contextual Understanding:
    • AI-generated code may not always integrate seamlessly with the broader project context, potentially leading to fragmented code.
  5. Bias in Training Data:
    • AI outputs can reflect biases present in their training data, potentially leading to non-inclusive code practices.

While AI code assistants offer significant productivity gains and economic benefits, they also present challenges that need careful consideration. Developers and organizations must balance the advantages with the potential risks, ensuring responsible use of these powerful tools.

Future of Code Automation

The future of AI code assistants is poised for significant growth and evolution, with technological advancements and changing developer attitudes shaping their trajectory towards potential ubiquity or obsolescence.

Technological Advancements on the Horizon:

  1. Enhanced Contextual Understanding: Future AI assistants are expected to gain deeper comprehension of project structures, coding patterns, and business logic. This will enable more accurate and context-aware code suggestions, reducing the need for extensive human review.
  2. Multi-Modal AI: Integration of natural language processing, computer vision, and code analysis will allow AI assistants to understand and generate code based on diverse inputs, including voice commands, sketches, and high-level descriptions.
  3. Autonomous Code Generation: By 2027, we may see AI agents capable of handling entire segments of a project with minimal oversight, potentially scaffolding entire applications from natural language descriptions.
  4. Self-Improving AI: Machine learning models that continuously learn from developer interactions and feedback will lead to increasingly accurate and personalized code suggestions over time.

Adoption Barriers and Enablers:

Barriers:

  1. Data Privacy Concerns: Organizations remain cautious about sharing proprietary code with cloud-based AI services.
  2. Integration Challenges: Seamless integration with existing development workflows and tools is crucial for widespread adoption.
  3. Skill Erosion Fears: Concerns about over-reliance on AI leading to a decline in fundamental coding skills among developers.

Enablers:

  1. Open-Source Models: The development of powerful open-source AI models may address privacy concerns and increase accessibility.
  2. IDE Integration: Deeper integration with popular integrated development environments will streamline adoption.
  3. Demonstrable ROI: Clear evidence of productivity gains and cost savings will drive enterprise adoption.
  1. AI-Driven Architecture Design: AI assistants may evolve to suggest optimal system architectures based on project requirements and best practices.
  2. Automated Code Refactoring: AI tools will increasingly offer intelligent refactoring suggestions to improve code quality and maintainability.
  3. Predictive Bug Detection: Advanced AI models will predict potential bugs and security vulnerabilities before they manifest in production environments.
  4. Cross-Language Translation: AI assistants will facilitate seamless translation between programming languages, enabling easier migration and interoperability.
  5. AI-Human Pair Programming: More sophisticated AI agents may act as virtual pair programming partners, offering real-time guidance and code reviews.
  6. Ethical AI Coding: Future AI assistants will incorporate ethical considerations, suggesting inclusive and bias-free code practices.

As these trends unfold, the role of human developers is likely to shift towards higher-level problem-solving, creative design, and AI oversight. By 2025, it’s projected that over 70% of professional software developers will regularly collaborate with AI agents in their coding workflows1. However, the path to ubiquity will depend on addressing key challenges such as reliability, security, and maintaining a balance between AI assistance and human expertise.

The future outlook for AI code assistants is one of transformative potential, with the technology poised to become an integral part of the software development landscape. As these tools continue to evolve, they will likely reshape team structures, development methodologies, and the very nature of coding itself.

Conclusion: A Tool, Not a Panacea

AI code assistants have irrevocably altered software development, delivering measurable productivity gains but introducing new technical and societal challenges. Current metrics suggest they are transitioning from novel aids to essential utilities—63% of enterprises now mandate their use. However, their ascendancy as the de facto standard hinges on addressing security flaws, mitigating cognitive erosion, and fostering equitable upskilling. For organizations, the optimal path lies in balanced integration: harnessing AI’s speed while preserving human ingenuity. As generative models evolve, developers who master this symbiosis will define the next epoch of software engineering.

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