This video is an introductory presentation about FPGA and programmable logic technology. I delivered this 45 minutes talk at an event hosted by 7 Peaks Software in Bangkok, Thailand, on November 19th, There is a wide range of applications for FPGA technology. Products ranging from satellites in space to trading robots on Wall Street utilize programmable logic. Here follow some industries that are known to be heavy users of FPGAs.
Open Source GPU Released
The defense industry is in an excellent position to benefit from custom FPGA implementations. They have lots of money and high requirements for quality. The spec lists for their products are often extreme because military-grade equipment is a tier of reliable engineering well above the consumer and industrial grades. Their budgets are always high and sometimes virtually unlimited, coming from a stable source like government backing. All of the above are reasons why FPGAs are used a lot for defense applications.
Not only for weapons but also for things like radio communication devices and test equipment. FPGAs are used extensively throughout the space industry. There are many reasons why they are a good fit for satellites. Many electronic designs in space are for things like interface control, reading of sensor data, signal processing, or control systems, tasks that suit an FPGA well.
Furthermore, space applications often fall under the same reliability requirements that are standard in the aerospace industry. Proving that a computer program has no unintended consequences is difficult and time-consuming.
International standards for airborne systems mandate that electronics used in aircraft must follow strict verification requirements. In most cases, it is easier and cheaper to satisfy the requirement of the hardware standard than for a corresponding software implementation.
You can even find FPGA accelerated communication and entertainment systems in modern cars. Furthermore, electric and hybrid vehicles are likely to use FPGAs for motor control tasks. Three-phase induction motors require strict timing control of the magnetic fields as the motor rotates.
Any inaccuracy will translate into loss of power efficiency. The telecommunication infrastructure utilizes a lot of FPGA technology. As already mentioned, FPGAs are standard in space applications like communication satellites. For consumer telecom equipment like mobile phones, FPGAs are not that common. Even though the initial cost of ASIC production is much higher than an FPGA, they are still economical because of the high sales volumes of mobile phones.
FPGAs are essential for them to be able to handle such high volumes of data with such low latency. Still, judging by circumstantial evidence like job listings, it becomes evident that they rely heavily on FPGAs. Algorithmic high-frequency stock market trading HFT is all about crunching the numbers faster than the competition.
Companies who engage in these activities do everything in their power to lower the latency of their processing pipelines. Their servers sit physically close to the stock exchange, and they invest in the fastest computer hardware money can buy. Then they can run the statistical analysis on incoming market data marginally faster and make more money. The economics of cryptocurrency mining is all about managing the electricity bill.
The coin has got to be worth more than what you paid for the electricity to mine it for it to be profitable. FPGAs are not that commonly found in consumer electronics. Still, there are plenty of examples of it. Here follow a few products you may have heard of that contain FPGAs.
It is unclear what the purpose of the FPGAs are in this design. The project is still in the development stage, and FPGAs are great for prototyping.
It is uncommon to find an FPGA in a mobile phone.Previously, these FPGAs were mainly used in electronics engineering, but not so much in software engineering. If you want to compute something, the common approach is to write some software for an instruction based architecture, such as a CPU or GPU. Another, more arduous, route one could take is to design a special circuit for this specific computation — as opposed to writing instructions for a general purpose circuit such as a CPU or GPU.
Once you have designed this circuit, you need some way to implement the design so that you can actually compute something. One way, which requires quite deep pockets, is to actually produce a circuit that implements this design this is called an Application Specific Integrated Circuit or ASIC. An easier way, and the main topic of this blog, is to implement your circuit design is to use a Field Programmable Gate Array FPGAa reconfigurable integrated circuit.
This is quite a bit different than the instruction-based hardware most programmers are used to, such as CPUs and GPUs. Instruction-based hardware is configured via softwarewhereas FPGAs are instead configured by specifying a hardware circuit.
Low latency is what you need if you are programming the autopilot of a jet fighter or a high-frequency algorithmic trading engine: the time between an input and its response as short as possible. With an FPGA it is feasible to get a latency around or below 1 microsecond, whereas with a CPU a latency smaller than 50 microseconds is already very good. Moreover, the latency of an FPGA is much more deterministic. One of the main reasons for this low latency is that FPGAs can be much more specialized: they do not depend on the generic operating system, and communication does not have to go via generic buses such as USB or PCIe.
On an FPGA, you can hook up any data source, such as a network interface or sensor, directly to the pins of the chip. A direct connection to the pins of the chip gives very high bandwidth as well as low latency. In these applications there are a lot of specialized sensors in the field, which generate an enormous amount of data.
How To Create a General Purpose Graphics Card With an FPGA
The volume of data needs to be reduced before being sent off, to make it more manageable. From a theoretical perspective, both hardware description languages and programming languages can be use to express any computation both are Turing completebut the difference in engineering details is vast. However, even when using such languages, programming FPGAs is still an order of magnitude more difficult than programming instruction based systems. A large part of the difficulty of programming FPGAs are the long compilation times.
This is due to the place-and-route phase : the custom circuit that we want needs to be mapped to the FPGA resources that we havewith paths as short as possible. This is a complex optimization problem which requires significant computation. Intel does offer an emulator, so testing for correctness does not require this long step, but determining and optimizing performance does require these overnight compile phases.
However, the situation is really not that clear cut, especially when it comes to floating point computations, but let us first consider situations where FPGAs are clearly more energy efficient than a CPU or GPU. Where FPGAs shine in terms of energy efficiency is at logic and fixed precision as opposed to floating point computations.
In crypto-currency such as bitcoin mining, it is exactly this property that makes FPGAs advantageous. In fact, everyone used to mine bitcoin on FPGAs. Which are special integrated circuits built for just one purpose. ASICs are an even more energy efficient solution but require a very large upfront investment for the design and large number of chips produced to be cost effective. But back to FPGAs. A lot of high performance computing use cases, such as deep learning, often depend on floating point arithmetic — something GPUs are very good at.
In the past, FPGAs were pretty inefficient for floating point computations because a floating point unit had to be assembled from logic blocks, costing a lot of resources. Does the addition of floating point units make FPGAs interesting for floating point computations in terms of energy efficiency?
Are they more energy efficient than a GPU? The fastest professional GPU that is available now is the Tesla Vwhich has a theoretical maximum of 15 TFLOPS Tera-floating-point-operations per second, a standard means of measuring floating point performance and uses about Watts of power. This card has a theoretical maximum of 9.So far, there are five parts.
Testing will be a recurring theme throughout the rest of the posts. In Part Three[Domipheus] works through his choices for the instruction set and starts writes up the instruction set decoder. Part Five builds up a bare-bones control unit and connects the decoder, ALU, and registers together to do some math and count up.
Yeah, RAM was not something I was including as useless crap. I was talking about junk like 7 segment displays, etc. If the RAM needs are pretty small kB range then you can implement them using lookup tables. I have around of them in a box in storage. I would love to be rid of them.
They have a Spartan 5 or 6 in them. The Gen 2 devices having a DVI hookup. If you look on ebay you can see examples, we pulled them all from service when the company closed its doors. A new company purchased the rights about a year after they closed shop.
Anyone that wants to pay shipping from is welcome to them. Try contacting me via cvmagic mailinator. Would you be able to ship one to Germany? I would pay for shipping of course. You can email me at my username at gmail. If you want to get in touch with me, you can send me some info at m8r-5sud6p mailinator. Sorry if this is a repost, I think the spam filters prevented my last request from posting. Hello, still have those modules? You can email me directly the info.
Not sure how to get you my contact info. Sent you a message, but received a reply that delivery was delayed. Is everything ok?Not bad for a failed Kickstarter, right? Problem is the popular codecs need royalties paid, and nobody wants an HTPC that can only play open source codecs.Graphics Processor (GPU) implementation in an FPGA (Altera Cyclone V)
Yeah I guess technically the MPEG-LA could go after everyone who distributes or perhaps even uses ffmpeg binaries, but I guess it would be a bit of a wasted effort, like trying to stop music piracy.
Ha Ha, he beat me to it. Oh well, I guess mine would have had vertex and pixel shaders. Good work! I gotta see how if? You should still release your work. I generally consider non-programmable devices to be video controllers or accelerators, since their behavior is dictated solely through registers. A GPU is just a very specialized processor. I can see uses for this as part of a dynamically configured bit of hardware. Feel like playing Quake? What else would you choose to do the development on?
FPGA based prototypes are nice because they can be turned into ICs eventually given enough demand at least. Metalized gate arrays are a nice inbetween for lower quantity production runs. Even low end FPGAs can contain the equivalent of hundreds of thousands of logic gates plus SRAM and multipliers, which is plenty for a basic processor.
Need special hardware accelerated instructions, a powerful coprocessor or full custom peripherals? An FPGA can do that. The upfront costs of a custom chip in a modern process are in the tens of millions of dollars. Large FPGAs are often used to prototype such custom chips. Now if you can just use a standard processor or microcontroller in your project, then it is probably cheaper and easier to do so. But FPGAs are the only hobbyist accessible way to have a lot of custom logic running at high speed.
All points true.Not bad for a failed Kickstarter, right? Problem is the popular codecs need royalties paid, and nobody wants an HTPC that can only play open source codecs. Yeah I guess technically the MPEG-LA could go after everyone who distributes or perhaps even uses ffmpeg binaries, but I guess it would be a bit of a wasted effort, like trying to stop music piracy.
Ha Ha, he beat me to it. Oh well, I guess mine would have had vertex and pixel shaders.
Good work! I gotta see how if? You should still release your work. I generally consider non-programmable devices to be video controllers or accelerators, since their behavior is dictated solely through registers. A GPU is just a very specialized processor. I can see uses for this as part of a dynamically configured bit of hardware.
Feel like playing Quake? What else would you choose to do the development on? FPGA based prototypes are nice because they can be turned into ICs eventually given enough demand at least. Metalized gate arrays are a nice inbetween for lower quantity production runs. Even low end FPGAs can contain the equivalent of hundreds of thousands of logic gates plus SRAM and multipliers, which is plenty for a basic processor.
Need special hardware accelerated instructions, a powerful coprocessor or full custom peripherals? An FPGA can do that. The upfront costs of a custom chip in a modern process are in the tens of millions of dollars.
Large FPGAs are often used to prototype such custom chips. Now if you can just use a standard processor or microcontroller in your project, then it is probably cheaper and easier to do so. But FPGAs are the only hobbyist accessible way to have a lot of custom logic running at high speed. All points true. And an Intel i7 is a far better processing tool than a ATtiny13 — so why bother with the later?
However, GPUs can be made massively parallel. The modern way to do things OpenGL, Direct3D uses two programs shaders that run on many instances, like threads if you like.
Wont that chew the FPGA up rather savagely? Might one prefer to swap that out for a bram based fifo implementation of somekind? I just checked. They never did finish a complete OpenGL for the Verite and IIRC they did for the Verite No more chips based on the technology, no finished drivers, nothing. You could maaayybe run Quake 3 with it. He could have used something with a Mali GPU that has open drivers but I guess the openness of the of the rest of the i. This is great — there is just not a lot of material out there beyond various VGA test pattern generators… this is an awesome release.
This site uses Akismet to reduce spam.The above is the fundamental reason for this series of posts. However, as a software engineer, you are limited in exposure to what that actually boils down to at the hardware level.
VHDL, really, is simple. You can get many other types of board even from Amazonand entry level ones are pretty affordable. As I write this now, the project is running code under simulation with basic arithmetic operations, addition, branching and memory access. I hope to learn much along the way whilst writing these articles. If you are an experienced hardware engineer and see me doing something a stupid, b inefficient, c unwise or d stupid, please do tell me by ways of twitter at domipheus.
This is not a superscalar processor. This will be a CPU that takes multiple instructions to execute the simplest of instructions. It comes with a very nice IDE, and associated toolchain — including a simulator. The next part will be about implementing the ram and register file, and testing it in the simulator.
For now, here is a spoiler of the TPU in some simulator action, bonus points goes to the people who realise there is an odd thing about the form of the baz branch if Ra is zero instruction.
Thanks for reading, send all comments to domipheus on twitter! Ohh, and before I forget, this willl be completely open source. Part 2 in this series is now available.
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It is optimized for use cases like deep learning and image processing.
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It can be used to experiment with microarchitectural and instruction set design tradeoffs. The following instructions explain how to set up the Nyuzi development environment. This includes an emulator and cycle-accurate hardware simulator, which allow hardware and software development without an FPGA, as well as scripts and components to run on FPGA.
This requires Ubuntu 16 Xenial Xeres or later to get the proper package versions. It should work for other distributions, but you will probably need to change some package names. From a terminal, execute the following:. Emacs is used for verilog-mode AUTO macros. The makefile executes this operation in batch mode. Then install the command line compiler tools by opening Terminal and typing the following:.
Alternatively, you could use MacPorts if that is installed on your system, but you will need to change some of the package names. You may optionally install GTKWave for analyzing waveform files. I have not tested this on Windows. Many of the libraries are cross platform, so it should be possible to port it.
But the easiest route is probably to run Linux under a virtual machine like VirtualBox. The following script will download and install the Nyuzi toolchain and Verilator Verilog simulator. Although some Linux package managers have Verilator, they have old versions. It will ask for your root password a few times to install stuff. If you are on a Linux distribution that defaults to python3, you may run into build problems with the compiler.
Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. GPGPU microprocessor architecture. Branch: master.
Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Latest commit c9e Apr 5,