[Bruce Land] of Cornell University will be a familiar name to many Hackaday readers, searching the site for ‘ECE4760′ will bring up many interesting topics around embedded programming. Every year [Bruce] releases yet more of the students’ work out into the wild to our great delight. This RP2040-based project is a bit more abstract than some previous work and shows yet another implementation of an older hack to utilise the DMA hardware of the RP2040 as another CPU core. While the primary focus of the RP2040 DMA subsystem is moving data between memory spaces, with minimal CPU intervention, the DMA control blocks have some fairly complex behaviour. This allows for a Turing-complete CPU to be implemented purely with the DMA hardware and a sprinkling of memory.

The method ties up three of the twelve DMA channels, and is estimated to have a similar performance to ‘an Arduino’ but [Bruce] doesn’t specify which one of the varied models that could be. But who cares anyway? Programming the CPU is a matter of leveraging the behaviour of the hardware, which is all memory mapped and targetable by the DMA. For example, the CPU can waggle GPIO pins by using the DMA to write values to the peripheral address space. The basic flow can be seen in the image above. DMA0 is used as the program counter, which points DMA1 to an array of DMA control blocks, a sequence of which codes for some of the ‘opcodes’ of the CPU model. DMA0 chains to (hands over control to) DMA1 which reads the control blocks and configures itself accordingly. DMA1 performs whatever data move is programmed, chains to DMA2, which in turn reprograms the DMA0 program counter to point to the next block in the list to be executed by DMA1.

By also using DMA1 to modify subsequent DMA1 control blocks (that’s self-modifying code happening there!) the system can implement more useful operations such as addition, logical operations, and conditional branching. Transport-triggered operations in certain shadow registers enable atomic set, reset, and XOR operations. All clever stuff, and a wonderful student project to have been involved with. [Bruce] points out a paper (using the Pi2) from the WOOT 2015 workshop which might offer a better explanation of this whole process.

If you’re still wondering who [Bruce Land] is and want a bit of a primer on some of these topics, then check out our previous coverage. If this theoretical stuff is a bit heavy (i.e. boring) then some of the projects have a more practical bent, such as the critical task of colour-sorting skittles.

Thumbnail image: Thomas Glau, CC BY-SA 4.0.

[Lewin] wrote in to tell us about a high speed library for Arduino Due that he helped develop which allows interfacing OLED displays that use the SSD1306 display controller, using DMA routines for faster display refresh time.

Typically, displays such as the Monochrome 1.3″ 128×64 OLED graphic display , are interfaced with an Arduino board via the SPI or I2C bus. The Adafruit_SSD1306 library written by [Limor Fried] makes it simple to use these displays with a variety of Arduinos, using either software or hardware SPI. With standard settings using hardware SPI, calls to display() take about 2ms on the Due.

[Lewin] wanted to make it faster, and the SAM3X8E on the Due seemed like it could deliver. He first did a search to find out if this was already done, but came up blank. He did find [Marek Buriak]’s library for ILI9341-based TFT screens. [Marek] used code from [William Greiman], who developed SD card libraries for the Arduino. [William] had taken advantage of the SAM3X8E’s DMA capabilities to enable faster SD card transfers, and [Marek] then adapted this code to allow faster writes to ILI9341-based screens. All [Lewin] had to do was to find the code that sent a buffer out over SPI using DMA in Marek’s code, and adapt that to the Adafruit library for the SSD1306.

There is a caveat though: using this library will likely cause trouble if you are also using SPI to interface to other hardware, since the regular SPI.h library will no longer work in tandem with [Lewin]’s library. He offers some tips on how to overcome these issues, and would welcome any feedback or testing to help improve the code. The speed improvement is substantial. Up to 4 times quicker using standard SPI clock, or 8 times if you increase SPI clock speed. The code is available on his Github repo.