Velocity and Force Monitoring System Design

Created a thread; Forked a project
Tablet USB AI Accelerator - KL720
ESP32 Multimodal Health Monitor with Pulse Oximetry, ECG, and Barometric Sensing
KL720 migration planning
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flux

Created a writable KL720 accelerator fork, corrected its description, and saved the Tablet AI Accelerator Migration Plan. The inherited hardware remains unchanged.
Open item
  • Exact tablet model/OS and representative AI model are needed before safely redesigning the schematic.
Next steps
  1. Confirm the tablet model.
  2. Select the first AI model and input data type.
  3. Prototype it using a KL720 development dongle.
  4. Once proven, replace the FPGA/DDR architecture with the KL720 USB‑C design.
S

sendikram

Android 15 and up
octa Kore KXD-103 PW2

flux

I’ll verify the KXD‑103 PW2’s USB host, SuperSpeed, and power capabilities, then update the fork’s architecture and Android 15 compatibility requirements.
Created a thread
Update Android tablet constraints
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flux

I researched the Android 15/KXD tablet constraint and dispatched an update to the fork’s migration plan; that update completed. The exact KXD‑103 PW2 specification was not publicly verifiable, while closely related Kexidar tablets commonly expose USB 2.0 rather than SuperSpeed.
Key implications:
  • Design for USB 2.0 High-Speed first; treat USB 3 as optional until physically verified.
  • KL720 remains conditional because some firmware/model updates require SuperSpeed.
  • The Android application should use android.hardware.usb, USB permission handling, and bulk endpoints—without root or kernel drivers.
  • Do not start the custom PCB until a KL720 dongle successfully runs inference on this exact Android 15 tablet.
  • Next required inputs are a photo of Settings → About tablet/product label and the first intended AI model or input type.
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Schematic

Layout

3D View

Schematic of Spartan AI Accelerator
please explain what U4 does.
B

bubbleboi

@copilot please explain what U4 does.
1y

copilot

The component U4U4 in your project is an XC7S25-1CSGA324I, which is a Spartan-7 FPGA from AMD. Here are some key functions and applications of this component:
  • Programmable Logic Device: FPGAs are programmable logic devices that can be configured to perform complex logic operations and algorithms. They are used to implement digital circuits and can be reprogrammed to adapt to new tasks.
  • High-Speed Operations: The Spartan-7 series is designed for high-speed and efficient processing, making it suitable for applications involving real-time data processing, signal processing, and communications.
  • I/O Support: This FPGA provides extensive I/O capabilities, allowing for the interfacing with various peripherals and devices. It supports numerous pins that can be configured for input or output operations.
  • Memory Interface: The FPGA can interface with external memory devices like DDR memory, which is crucial for applications that require large amounts of data storage and fast data retrieval.
  • Low Power Consumption: The Spartan-7 family is known for its low power consumption, which is advantageous in power-sensitive applications, such as portable or battery-operated devices.
  • Security Features: It includes features for secure boot and encryption, beneficial in applications where data security is a concern.
Given these features, the XC7S25-1CSGA324I FPGA in your project likely serves as the brain of the Spartan AI Accelerator, handling complex computations required for AI tasks while interfacing with other components like I/O and memory. If you have more specific applications in your design, feel free to share!
1y
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explain this project.
J

jochensupper

@copilot explain this project.
1y

copilot

The project is called the "Spartan AI Accelerator," designed to enhance artificial intelligence processing capabilities using an AMD Spartan-7 FPGA. The project is optimized for size, cost, and performance, leveraging the FPGA's capabilities to handle efficient AI tasks while supporting extensive I/O and DDR memory interfacing for high-speed computations.
Key Components:
  • FPGA: XC7S25-1CSGA324I
    • Central processing unit for AI tasks. It handles various I/O and connects to DDR memory for high-speed computations.
  • DDR Memory: MT41K256M16TW-107:P
    • Provides fast DDR memory access essential for complex AI algorithms.
  • Flash Memory: S25FL128SAGNFI000
    • Used for storing firmware and accelerating boot processes of the FPGA.
  • Voltage Regulator: AP2303MPTR-G1
    • Provides stable power supply necessary for FPGA and memory operation.
  • Inductors and Capacitors:
    • Various capacitors (from 0.1μF to 100μF) and inductors (1μH) are used for power smoothing, decoupling, and stabilization across the PCB.
  • Connectors:
    • Includes multiple connectors and nets for interfacing with external devices, likely facilitating high-speed communication and data processing.
  • Protection and Connectivity:
    • ESD diodes like the ESD8104MUTAG ensure device protection against electrostatic discharge, especially important in high-speed data transfer interfaces like USB and Type-C connectors like TYPE-C-31-M-12.
Design:
  • The project contains numerous resistors configured mostly in standard values like 10kΩ or 56kΩ for pull-ups or pull-downs, which are common practice in designing stable and reliable digital circuits connected to FPGAs.
  • The involvement of nets, ions, and pins shows a complex circuit designed to interface with different components, handling multiple signals at high bandwidths.
Additional Functionalities:
  • Power and signal conditioning components like LDOs, GPIOs managed by elements such as DA9062-00AM2 are included to ensure the FPGA functions correctly under different loads and logic levels.
The Spartan AI Accelerator's architecture enables it to perform efficiently across AI tasks by combining high-speed logic processing with robust support circuitry, enhancing its capability as a compact, cost-effective AI computation module.
1y
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please explain what R59, C12, U3, XADCVREF, C11, C58 do.
S

sashashad41

@copilot please explain what R59, C12, U3, XADCVREF, C11, C58 do.
1y

copilot

Certainly! Let's break down the components and their roles in your Spartan AI Accelerator project:
  • R59 (200k-ohm resistor): Connected to the IREF pin of the PMIC (U3), typically used as a reference current setting. This resistor value might define a specific current output or function for the PMIC.
  • C12 (470nF capacitor): Connected to VBBAT pin of U3, this capacitor is likely used for stabilizing the input voltage by filtering out noise or transient signals that might affect the PMIC's performance.
  • U3 (PMIC - DA9062-00AM2): This is a Power Management IC from Renesas, used for providing various voltage levels. It includes multiple LDOs, buck converters, and other control functions. It is central to managing the power distribution and regulation in your circuit.
  • XADCVREF (Reference Voltage Net): This is commonly a reference voltage for ADC (Analog-to-Digital Converter) operations. It provides a stable reference against which analog signals are measured.
  • C11 (2.2uF capacitor): Typically used for decoupling/filtering purposes. In this case, it's connected to the VREF pin of the PMIC, ensuring a stable reference voltage output without voltage ripple.
  • C58 (10uF capacitor): Often larger capacitors like this are used for smoothing and removing low-frequency noise from power lines, especially important for sensitive circuits like those needing ADC reference voltage stability.
What these components collectively do:
This set of components forms part of the power management and regulation section in your design. Together, they ensure that your power supply is stable, filtered, and noise-free, which is critical for reliable and accurate operation, particularly in systems leveraging ADCs or other precision modules. Does that make sense for your design intent? Any particular part you need more insights on?
1y
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How work this device?
S

shadivl

@copilot How work this device?
1y

copilot

The Spartan AI Accelerator is designed around the AMD Spartan-7 FPGA, facilitating efficient AI processing with optimized size, cost, and performance. The core functionalities are centered on the integration and interaction between various components:
  1. FPGA (Field Programmable Gate Array): The main component is the XC7S25-1CSGA324I, responsible for processing and handling AI tasks. FPGAs are highly flexible and can be programmed to perform complex algorithms efficiently.
  2. Memory Interfacing: The design includes the MT41K256M16TW-107:P DDR memory, which supports high-speed data transfer and storage, critical for AI computations that demand rapid access to large datasets.
  3. Power Management: The design incorporates voltage regulators like the AP2303MPTR-G1 and references like the REF3012AIDBZR to ensure stable power delivery to sensitive components.
  4. Peripheral Connections: Various nets, such as QSPI interfaces for non-volatile memory (S25FL128SAGNFI000), allow for the storage of configuration and data.
  5. Protection and Filtering: Components like the ESD8104MUTAG provide electrostatic discharge protection to safeguard the device against voltage spikes in I/O connections.
  6. External Interfaces: The design includes connectors for external interaction, including USB and other interfaces for programming and communication.
This device is aimed at providing a flexible platform for AI acceleration, supporting extensive I/O and high-speed computation typical of AI workloads.
If you have specific questions about functionality or need details about a particular component's role, feel free to ask!
1y
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C86
Capacitance
10nF
C110
Capacitance
470nF
C91
Capacitance
470nF
C106
Capacitance
47nF
C116
Capacitance
470nF
C102
Capacitance
47nF
C105
Capacitance
47nF
C94
Capacitance
470nF
C83
Capacitance
470nF
C104
Capacitance
47nF
C103
Capacitance
47nF
C112
Capacitance
470nF
C70
Capacitance
47nF
C101
Capacitance
47nF
C100
Capacitance
47nF
C115
Capacitance
470nF
C19
Capacitance
0.1uF
C85
Capacitance
470nF
C81
Capacitance
47nF
C89
Capacitance
470nF
C87
Capacitance
10nF
C92
Capacitance
470nF
C80
Capacitance
47nF
C90
Capacitance
47nF
C107
Capacitance
47nF
C88
Capacitance
47nF
C109
Capacitance
47nF
C96
Capacitance
470nF
C113
Capacitance
47uF
C82
Capacitance
100uF
C93
Capacitance
100uF
C99
Capacitance
4.7uF
C84
Capacitance
4.7uF
C95
Capacitance
4.7uF
C111
Capacitance
4.7uF
C38
Capacitance
10uF
C108
Capacitance
4.7uF
U4

Spartan AI Accelerator