March 12, 2016

AHCI/SATA stack under GNU GPL

by Andrey Filippov

Implementation includes AHCI SATA host adapter in Verilog under GNU GPLv3+ and a software driver for GNU/Linux running on Xilinx Zynq. Complete project is simulated with Icarus Verilog, no encrypted modules are required.

This concludes the last major FPGA development step in our race against finished camera parts and boards already arriving to Elphel facility before the NC393 can be shipped to our customers.

Fig. 1. AHCI Host Adapter block diagram

Fig. 1. AHCI Host Adapter block diagram

Why did we need SATA?

Elphel cameras started as network cameras – devices attached to and controlled over the Ethernet, the previous generations used 100Mbps connection (limited by the SoC hardware), and NC393 uses GigE. But this bandwidth is still not sufficient as many camera applications require high image quality (compared to “raw”) without compression artifacts that are always present (even if not noticeable by the human viewer) with the video codecs. Recording video/images to some storage media is definitely an option and we used it in the older camera too, but the SoC IDE controller limited the recording speed to just 16MB/s. It was about twice more than the 100Mb/s network, but still was a bottleneck for the system in many cases. The NC393 can generate 12 times the pixel rate (4 simultaneous channels instead of a single one, each running 3 times faster) of the NC353 so we need 200MB/s recording speed to keep the same compression quality at the increased maximal frame rate, higher recording rate that the modern SSD are capable of is very desirable.

Fig.2. SATA routing

Fig.2. SATA routing: a) Camera records data to the internal SSD; b) Host computer connects directly to the internal SSD; c) Camera records to the external mass storage device

The most universal ways to attach mass storage device to the camera would be USB, SATA and PCIe. USB-2 is too slow, USB-3 is not available in Xilinx Zynq that we use. So what remains are SATA and PCIe. Both interfaces are possible to implement in Zynq, but PCIe (being faster as it uses multiple lanes) is good for the internal storage while SATA (in the form of eSATA) can be used to connect external storage devices too. We may consider adding PCIe capability to boost recording speed, but for initial implementation the SATA seems to be more universal, especially when using a trick we tested in Eyesis series of cameras for fast unloading of the recorded data.

Routing SATA in the camera

It is a solution similar to USB On-The-Go (similar term for SATA is used for unrelated devices), where the same connector is used to interface a smartphone to the host PC (PC is a host, a smartphone – a device) and to connect a keyboard or other device when a phone becomes a host. In contrast to the USB cables the eSATA ones always had identical connectors on both ends so nothing prevented to physically link two computers or two external drives together. As eSATA does not carry power it is safe to do, but nothing will work – two computers will not talk to each other and the storage devices will not be able to copy data between them. One of the reasons is that two signal pairs in SATA cable are uni-directional – pair A is output for the host and input for device, pair B – the opposite.

Camera uses Vitesse (now Microsemi) VSC3304 crosspoint switch (Eyesis uses larger VSC3312) that has a very useful feature – it has reversible I/O ports, so the same physical pins can be configured as inputs or outputs, making it possible to use a single eSATA connector in both host and device mode. Additionally VSC3304 allows to change the output signal level (eSATA requires higher swing than the internal SATA) and perform analog signal correction on both inputs and outputs facilitating maintaining signal integrity between attached SATA devices.

Aren’t SATA implementations for Xilinx Zynq already available?

Yes and no. When starting the NC393 development I contacted Ashwin Mendon who already had SATA-2 working on Xilinx Virtex. The code is available on OpenCores under GNU GPL license. There is an article published by IEEE . The article turned out to be very useful for our work, but the code itself had to be mostly re-written – it was still for different hardware and were not able to simulate the core as it depends on Xilinx proprietary encrypted primitives – a feature not compatible with the free software simulators we use.

Other implementations we could find (including complete commercial solution for Xilinx Zynq) have licenses not compatible with the GNU GPLv3+, and as the FPGA code is “compiled” to a single “binary” (bitstream file) it is not possible to mix free and proprietary code in the same design.

December 21, 2015

X3D assemblies from any CAD

by Andrey Filippov

Converting mechanical assemblies to X3D models from STEP (ISO 10303) files

Like all manufacturing companies we use mechanical CAD program to design our products. We would love to use Free Software programs for that, but so far even FreeCAD has a warning on their download page “FreeCAD is under heavy development and might not be ready for production use”. We have to use proprietary tools, our choice was the program that natively runs on GNU/Linux we use on our computers. This program generates STEP files that we can send to virtually any machine shop (locally or overseas) and expect to receive the manufactured parts that match our design. For the last 6 years we kept the CAD models for all the camera parts on Elphel Wiki hoping they might be needed not only by the machine shops we order parts from, but also by our users to incorporate (or modify) our products in their systems.

November 12, 2015

NC393 progress update: 14MPix Sensor Front End is up and running

by Andrey Filippov

10398 Sensor Front End with 14MPix MT9F002

10398 Sensor Front End with 14MPix MT9F002

Sensors (ON Semiconductor MT9F002) and blank PCBs arrived in time and so I was able to hand-assemble two 10398 boards and start testing them. I had some minor problems getting data output from the first board, but it turned out to be just my bad soldering of the sensor, the second board worked immediately. To my surprise I did not have any problems with HiSPi decoder that I simulated using the sensor model I wrote myself from the documentation, so the color bar test pattern appeared almost immediately, followed by the real acquired images. I kept most of the sensor settings unmodified from the default values, just selected the correct PLL multiplier, output signal levels (1.8V HiVCM – compatible with the FPGA) and packetized format, the only other registers I had to adjust manually were exposure and color analog gains.

As it was reasonable to expect, sensitivity of the 14MPix sensor is lower than that of the 5MPix MT9P006 – our initial estimate is that it is 4 times lower, but this needs more careful measurements to find out exposure required for pixel saturation with the same illumination. Analog channel gains for both sensors we set slightly higher than minimal ones for the saturation, but such rough measurements could easily miss a factor of 1.5. MT9F002 offers more controls over the signal chain gains, but any (even analog) gain in the chain that boosts signal above the minimal needed for saturation proportionally reduces used “well capacity”, while I expect the Full Well Capacity (FWC) is already not very high for the 1.4μm x1.4 μm pixel sensor. And decrease in the number of electrons stored in a pixel accordingly increases the relative shot noise that reveals itself in the highlight areas. We will need to accurately measure FWC of the MT9F002 and have better sensitivity comparison, including that of the binned mode, but I expect to find out that 5MPix sensor are not obsolete yet and for some applications may still have advantages over the newer sensors.

November 4, 2015

NC393 progress update: one gigapixel per second (12x faster than NC353)

by Andrey Filippov

All the PCBs for the new camera: 10393, 10389 and 10385 are modified to rev “A”, we already received the new boards from the factory and now are waiting for the first production batch to be build. The PCB changes are minor, just moving connectors away from the board edge to simplify mechanical design and improve thermal contact of the heat sink plate to the camera body. Additionally the 10389A got m2 connector instead of the mSATA to accommodate modern SSD.

While waiting for the production we designed a new sensor board (10398) that has exactly the same dimensions, same image sensor format as the current 10338E and so it is compatible with the hardware for the calibrated sensor front ends we use in photogrammetric cameras. The difference is that this MT9F002 is a 14 MPix device and has high-speed serial interface instead of the legacy parallel one. We expect to get the new boards and the sensors next week and will immediately start working with this new hardware.

September 28, 2015

Google is testing AI to respond to privacy requests

by Andrey Filippov

Robotic customer support fails while pretending to be an outsourced human. Last week I searched with Google for Elphel and I got a wrong spelled name, wrong address and a wrong phone number.

Google search for Elphel

Google search for Elphel

A week ago I tried Google Search for our company (usually I only check recent results using last week or last 3 days search) and noticed that on the first result page there is a Street View of my private residence, my home address pointing to a business with the name “El Phel, Inc”.

Yes, when we first registered Elphel in 2001 we used our home address, and even the first $30K check from Google for development of the Google Books camera came to this address, but it was never “El Phel, Inc.” Later wire transfers with payments to us for Google Books cameras as well as Street View ones were coming to a different address – 1405 W. 2200 S., Suite 205, West Valley City, Utah 84119. In 2012 we moved to the new building at 1455 W. 2200 S. as the old place was not big enough for the panoramic camera calibration.

I was not happy to see my house showing as the top result when searching for Elphel, it is both breach of my family privacy and it is making harm to Elphel business. Personally I would not consider a 14-year old company with international customer base a serious one if it is just a one-man home-based business. Sure you can get the similar Street View results for Google itself but it would not come out when you search for “Google”. Neither it would return wrongly spelled business name like “Goo & Gel, Inc.” and a phone number that belongs to a Baptist church in Lehi, Utah (update: they changed the phone number to the one of Elphel).

September 18, 2015

NC393 progress update: all hardware is operational

by Andrey Filippov

10393 with 4 image sensors

10393 with 4 image sensors

Finally all the parts of the NC393 prototype are tested and we now can make the circuit diagram, parts list and PCB layout of this board public. About the half of the board components were tested immediately when the prototype was built – it was almost two years ago – those tests did not require any FPGA code, just the initial software that was mostly already available from the distributions for the other boards based on the same Xilinx Zynq SoC. The only missing parts were the GPL-licensed initial bootloader and a few device drivers.

July 29, 2015

NC393 progress update and a second life of the NC353 FPGA code

by Andrey Filippov

Another update on the development of the NC393 camera: finished adding FPGA code that re-implements functionality of the NC353 camera (just with additional multi-sensor capability), including JPEG/JP4 compressors, IMU/GPS logger and inter-camera synchronization. Next step – simulation and debugging, and it will use co-simulating of the same sensor image data using the code of the existing NC353 camera. This involves updating of that camera code to the state compatible with the development tools we use, and so the additional sub-project was spawned.

June 9, 2015

NC393 progress update: HDL code for sensor channels is ported or re-written

by Andrey Filippov

Quick update: a new chunk of code is added to the NC393 camera FPGA project. It is a second (of three needed to match the existing NC353 functionality) major parts of the system after the memory controller is finished. This code is just written, it still has to be verified by the simulation first, and then by synthesizing and by running it on the actual hardware. We plan to do that when the third part – image compressors will be ported to the new system too. The added code deals with receiving data from the image sensors and pre-processing it before storing in the video memory. FPGA-based systems are very flexible and many other configurations like support of multi-lane serial interface sensors or using several camera ports to connect a single large high-speed sensor are possible and will be implemented later. The table below summarizes parameters of the current code only.

Table 1. NC393 Sensor Connections and Pre-processing
Feature Value
Number of sensor ports 4
Total number of multiplexed sensors 16
Total number of multiplexed sensors with existing 10359 multiplexer board 12
Sensor interface type (implemented in HDL) parallel 12 bits
Sensor interface hardware compatibility parallel LVCMOS/serial differential 8 lanes + clock
Sensor interface voltage levels programmable up to 3.3V
Number of I²C sequencers 4 (1 per port)
Number of I²C sequencers frames 16
Number of I²C sequencers commands per frame 64
I²C sequencers commands data width 16/8 bits
Image data width stored 16/8 bits per pixel
Gamma conversion regions per port 4
Histograms: number of rectangular ROI (Regions of Interest) per port 4
Histograms: number of color channels 4
Histograms: number of bins per color 256
Histograms: width per bin 18 or 32 bits
Histograms: number of histograms stored per sensor 16


May 9, 2015

NC393 Development progress: Multichannel memory controller for the multi-sensor camera

by Andrey Filippov

Development of the NC393 camera has just passed an important milestone  – we completed HDL code that constitutes the core of this new camera, tested most of the Zynq-specific features that were not available in the older Spartan-3 FPGA used in our current NC353 devices. Next development phase  will involve porting some of the existing code that deals with sensor interfacing, gamma correction, histograms, color conversion and JPEG/JP4 compression – code that was tested in the thousands of cameras and many billions of processed images, including the applications listed in Wikipedia. New camera is designed primarily for the multisensor applications – up to four connected directly to the system board and more through the multiplexers as we currently do in Eyesis4π cameras. It is the memory controller that had to be redesigned completely, the sensor and compressor channels can reuse most of the existing code and just have 4 instances of the same modules instead of a single one. Starting early this year I’ve got an opportunity to put aside other projects and work full time on the new camera code.

April 24, 2015

FPGA to DDR3 memory interface: step-by-step timing calibration and set up

by Andrey Filippov

Working with the DDR3 Memory interface I was not able to avoid the temptation to investigate more the very useful feature of the modern FPGA devices – individually programmed input/output delay elements on all (or at least many) of its pins. This is needed to both prepare to increase the memory clock frequency and to be able to individually adjust the timing on other pads, such as the sensor ports, especially when switching from the parallel to high speed serial interface of the modern image sensors.

Xilinx Zynq device we are using has both input and output delays on all low-voltage pins used for the memory interface in the camera, but only input ones on the higher voltage range I/O banks. Luckily enough image sensors connected to these banks need just that – data rate to the sensors is much lower than the rate of the data they generate and send to the FPGA.


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