There is  no such things as pin map.

They are all customized by FPGA VHDL files.  Research on source files is needed. The documents are not perfect, but the source file provide enough information already.

Here is some clue for you .

#include "common.h"


#ifndef __DEFINES_VIDOR_H__
#define __DEFINES_VIDOR_H__


#define FPGA_SPI_INTERFACES_COUNT 6
#define FPGA_I2C_INTERFACES_COUNT 4
#define FPGA_UART_INTERFACES_COUNT 9
#define FPGA_ENCODERS_COUNT 11
#define FPGA_NEOPIXEL_COUNT 4
#define FPGA_CAMERA_COUNT 0
#define FPGA_GFX_COUNT 0
#define FPGA_QR_COUNT 0


#define GPIO_NUM_OFFSET 100


#define IRQ_PIN 33


#define NEOPIXEL_PIN_0 A6
#define NEOPIXEL_PIN_1 0
#define NEOPIXEL_PIN_2 7
#define NEOPIXEL_PIN_3 8
#define NEOPIXEL_PIN_4 12


#define NEOPIXEL_PINMUX 5


// NINA signals
#define SPIWIFI              SPIEx
#define SerialNina           SerialEx
#define FPGA_NINA_TX         (64 + 16)
#define FPGA_NINA_RX         (64 + 15)
#define FPGA_NINA_MOSI       (64 + 19)
#define FPGA_NINA_MISO       (64 + 20)
#define FPGA_NINA_SCK        (64 + 18)
#define FPGA_NINA_GPIO0      (64 + 10)   // WM_PIO27 -> NiNa GPIO0 -> FPGA N9
#define FPGA_SPIWIFI_RESET   (64 + 0)    // WM_RESET -> NiNa RESETN -> FPGA R1
#define FPGA_SPIWIFI_ACK     (64 + 5)    // WM_PIO7  -> NiNa GPIO33 -> FPGA P6
#define FPGA_SPIWIFI_SS      (64 + 11)   // WM_PIO28 -> NiNa GPIO5 -> FPGA N11


#endif

or,

int VidorTwoWire::begin(void) {
  int mode;


  // Master Mode
  if (idx < 3) {
    mode = 4;
  }else{
    mode = 5;
  }
  if (sda == -1) {
    goto set_scl;
  }
  if (sda <= 14) {
    sda = sda-0;
    pinMode(sda,INPUT);
    pinMode(sda+140, mode);
  } else {
    sda = sda-A0;
    pinMode(sda,INPUT);
    pinMode(sda+133, mode);
  }


set_scl:
  if (scl == -1) {
    goto i2c_apply;
  }
  if (scl <= 14) {
    scl = scl-0;
    pinMode(scl, INPUT);
    pinMode(scl+140, mode);
  } else {
    scl = scl-A0;
    pinMode(scl, INPUT);
    pinMode(scl+133, mode);
  }


i2c_apply:
  uint32_t rpc[1];


  rpc[0] = MB_CMD(devIdx, idx, 0, 0x01);
  return VidorMailbox.sendCommand(rpc, 1);
}

Hope it can be of some help.

Has anyone found out the pin mapping of remaining FPGA pins as even after looking at schematics from Arduino website, I could not find out pin routing. The Verilog testing and learning is not helpful to me because there is  not other pin routing available. Anyone who knows other pin routing except 33, A0 please help?!

If you use LoRaWAN via TTN then there is a fair access policy which limits transmissions to one every minute.

There are also limits on 868MHz transmit duty cycles so may not be the best bet for audio because of this.

Congratulations to the selected challengers. It will be interesting to see how you tackle applications with this beast.

Thanks for the offer of a Vidor 4000 - that is very exciting. My original plan to look at CAN-bus car engine info via its OBD2 port may take a back seat. What I would really like to try is to make an electronic organ. I have been attempting this for years but technology moves on faster that I can get building. The last attempt was using a DSP chip. It could achieve about 60 tones simultaneously which is not really enough: a pipe organ can easily have hundreds of pipes keyed if all the stops are pulled out. Each pipe could have sine waves on fundamental and harmonic frequencies with the proportions stored in ROM. The FPGA's multipliers could be used to filter white noise (or pseudo-random bits) to obtain a simulation of a soft flue pipe.

Input would be MIDI serial data via an opto-coupler. Shame I got rid of my home-made MIDI keyboards a few years ago...

Congratulations to those selected for a starter pack of the MKR Vidor 4000 and I'm really interested to see how your projects develop and which of the Arduino tools and libraries you decide to use to configure your FPGA. Good luck to the selected members and also to anyone who decides to participate separately.

Rod

Title: New way to protect Forest animals System

Problem: In many situations it was evident that animals in forest crossing roads cause accidents to the travelers and sometimes they too have lost their lives due to severe accidents. In the forest prone area if the image and video processing analytic device is place at every 200-500 meters it gives indication to the travelers to slow down their vehicles. Wild animals like leopards, tigers, elephants, bears, wild boars attack the two wheelers, four wheeler small vehicles. In order to avoid such incidents a real-time imaging, detecting and tracking device is to be installed so that it warns the travelers to be careful. It also helps the forest rangers to identify their presence and take necessary action.

Plan: I would like to design a Smart Animals tracker to identify their presence and indicate the travelers to be careful over the region. It is our duty to monitor their presence and improvement within wildlife habitats. The road construction agencies have to show little interest towards this commitment to place the animal tracker and display their presence ahead.

Features: The Arduino MKR Vidor 4000 is a new kind of development board which combines the high performance and flexibility of an FPGA with the Arduino ease-of-use in a small form factor. This board hosts features: onboard 8 Mbyte SDRAM, 2 Mbyte QSPI Flash (1MB for user applications), Micro HDMI connector, MIPI camera connector, Wifi & BLE powered by U-BLOX NINA W102 module, the classic MKR interface on which all pins are driven both by SAMD21 and FPGA and a MiniPCI Express connector with up to 25 user programmable pins. The FPGA contains 16K Logic Elements, 504Kbit of embedded RAM and 56 18x18 bit HW multipliers for high-speed DSP and also can be configured for functions such as UARTs, (Q)SPI, high res/ high freq PWM, quadrature encoder, I2C, I2S, Sigma Delta DAC, etc. On board FPGA can be also used for high-speed DSP operations for audio and video processing. The Arduino MKR Vidor 4000 is programmed using the Arduino Software (IDE).

Design implementation: The on-board features and interfacing ports is used to design the advanced IoT wild animal tracker and make a caution on the display boards to improve the wildlife habitats. This is possible with the Arduino MKR Vidor 4000 kit because it has MIPI camera connector to interface the camera module to record the video of the animals presence. Micro HDMI connector is use to interface the display board to limit the vehicles speed, distance of animals presence and type of wild animal roaming. The on-board FPGA can perform the high-speed DSP operations for audio and video processing to recognize the race of the animal and count the number. Sometimes the visibility, temperature and humidity measurements are necessary to study the environmental conditions. In my opinion the Arduino MKR Vidor 4000 kit is a suitable device to design “New way to protect Forest animals system”.

Require hardware: Arduino MKR Vidor 4000 kit as the main core device, Arduino MKR connector carrier to interface the sensors, Arduino MKR MEM sheild to record the data for future analysis.

Conclusion: The New way to protect Forest animals system will be helpful to protect he wildlife and it is our primary duty to safe wildlife habitats. The necessary precautions should be taken while driving vehicles in the ghat areas. The animals doesn’t have a stipulated time to cross, so in-time identifying with Arduino MKR Vidor 4000 kit and alerting the passerby by displaying their status will be a helpful method to save their lives. It also saves our troubles caused by the wildlife accidents. The video and audio processing in the Arduino MKR Vidor 4000 kit is used to identify the race and their number and that can be displayed on the caution board.

A couple of points you might like to consider.

NXP offer a range of ARM based micros with a pin function matrix, which allows you to use most of the pins for almost anything.

The Ethernet shield will be of very limited use in debugging the FPGA, for effective FPGA debug you need access to the JTAG port and some of the general purpose IO pins.

The Vidor 4000 is currently a very non ideal development environment for FPGA - check out this article:

https://hackaday.com/2018/07/30/hands-on-with-new-arduino-fpga-board-mkr-vidor-4000/

Before you get too carried away with the FPGA idea, do please consider it's power consumption - a modest design might use 200mW but a busy design in this part might use more than 1W.

Here's a link to essential information on the Cyclone 10L FPGAs:

https://www.intel.com/content/www/us/en/programmable/products/fpga/cyclone-series/cyclone-10/cyclone-10-lp/support.html

MK

I propose a project that uses an Arduino MKR Vidor 4000 to include "on edge" Computer Vision processing (using a trained Deep Learning model if possible) for target tracking.

I work with a group of people in solving civil engineering problems using Computer Vision and Artificial Intelligence. We are planning to develop a non intrusive method to estimate the movement of moored cargo ships. Current methods, such as  Differential GPS are expensive. Other, such as IMUs are not suitable due to the huge magnetic interference that cargo ships have.

Using 3 targets (bull's-eye) in a vessel structure we can determine the 6DOF of the vessel movement (3 axial displacements and 3 rotations). The Arduino MKR Vidor 4000 would be used to process the images of a camera and calculate the targets position and a MKR ETH Shield would be used for transmitting the data allowing to monitor the ship in real time (port operators can block wifi and mobile signals). There are regulations that limit these movement, so this system would allow to estimate the movements in a fast and cheap manner. The project could have more applications due to the generality of the target tacking capability.

The ever-lower cost of microcontrollers, more efficient energy consumption and greater connectivity allowed the emergence of the Internet of Things concept, which consists of the use of computational elements in various objects of our daily life. These devices, in general, offer a reduced energy consumption implying in limited computational capacity. However, the advances observed in software, especially in the area of Artifical Intelligence, require considerable computational power. A widely used alternative is to allow the processing to be performed in the cloud and that only raw data collection or any system actuation is performed on the device. However, some applications have latency demands that can not be achieved using the cloud. The concepts of fog and edge computing allow meeting the delay requirements of these applications, as they advocate the partitioning of processing between local and cloud devices. In this way, temporarily critical sections of software run locally (at the edge or at the fog) without the need to send raw data to the cloud. In this context, the FPGA emerges as an excellent alternative because it is able to combine flexibility, low power consumption, and relative computational power, enabling the execution of computationally intensive tasks even in energy-restricted environments.

Our project aims to use the MKR Vidor 4000 and the MKR MEM and MKR Connector Carrier shields to build the prototype of an equipment to monitor the behavior of grazing animals. Identifying the behavior of grazing animals is essential for the proper and efficient animal management, in order to improve their comfort conditions and, consequently, the quality of the final product as well as productivity. This precise management is essential for, for example, the identification of heat in dairy cows, which must be done quickly for the breeder to provide artificial insemination, since the fertile period of these animals lasts less than a day. Monitoring other parameters such as how much time the animal spent feeding and ruminating as well as the time spent in idleness are other important variables. It is possible to identify these behaviors through sensors such as accelerometers, gyroscopes, piezo-electric vibration sensors, microphones, GPS, etc ... and process them through machine learning algorithms. In this project, we plan to use the MKR Connector Carrier shield to interface with these sensors and use the MKR MEM to record the raw data collected by the sensors on an SD card for further processing validation. The FPGA will be used to implement ML algorithms, or parts of them, in order to reduce the amount of processing in the cloud, of communication and consequently reduce the latency in the identification of the behavior of the animals.