Learn about parallel computing, the rise of heterogeneous processing (also known as hybrid processing), and the prospect of quantum engineering as a field of study!
Parallel computing used to be a way of sharing tasks between processor cores.
When processor clock rates stopped increasing, the response of the microprocessor companies was to increase the number of cores on a chip to increase throughput.
But now, the increased use of specialized processing elements has become more popular.
A GPU is a good example of this. A GPU is very different from an x86 or ARM processor and is tuned for a different type of processing.
GPUs are very good at matrix math and vector math. Originally, they were designed to process pixels. They use a lot of floating point math because the math behind how a pixel value is computed is very complex.
A GPU is very useful if you have a number of identical operations you have to calculate at the same time.
GPUs used to be external daughter cards, but in the last year or two the GPU manufacturers are starting to release low power parts suitable for embedded applications. They include several traditional cores and a GPU.
So, now you can build embedded systems that take advantage of machine learning algorithms that would have traditionally required too much processing power and too much thermal power.
This is an example of a heterogeneous processor (AMD) or hybrid processor. A heterogeneous processor contains cores of different types, and a software architect figures out which types of workloads are processed by which type of core.
Andrew Chen (professor) has predicted that this will increase in popularity because it’s become difficult to take advantage of shrinking the semiconductor feature size.
This year or next year, we will start to see heterogeneous processors (MOOR) with multiple types of cores.
Traditional processors are tuned for algorithms on integer and floating point operations where there isn’t an advantage to doing more than one thing at a time. The dependency chain is very linear.
A GPU is good at doing multiple computations at the same time so it can be useful when there aren’t tight dependency chains.
Neither processor is very good at doing real-time processing. If you have real time constraints – the latency between an ADC and the “answer” returned by the system must be short – there is a lot of computing required right now. So, a new type of digital hardware is required. Right now, ASICs and FPGAs tend to fill that gap, as we’ve discussed in the All about ASICs podcast.
Quantum cores (like we discussed in the what is quantum computing podcast) are something that we could see on processor boards at some point. Dedicated quantum computers that can exceed the performance of traditional computers will be introduced within the next 50 years, and as soon as the next 10 or 15 years.
To be a consumer product, a quantum computer would have to be a solid state device, but their existence is purely speculative at this point in time.
Quantum computing is reinventing how processing happens. And, quantum computers are going to tackle very different types of problems than conventional computers.
There is a catalog on the web of problems and algorithms that would be substantially better on a quantum on a computer than a traditional computer.
People are creating algorithms for computers that don’t even exist yet.
The Economist estimated that the total spend on quantum computing research is over 1 Billion dollars per year globally. A huge portion of that is generated by the promise of these algorithms and papers. The interest is driven by this.
Quantum computers will not completely replace typical processors.
Lee’s opinion is that the quantum computing industry is still very speculative, but the upsides are so great that neither the incumbent large computing companies nor the industrialized countries want to be left behind if it does take off.
The promise of quantum computing is beyond just the commercial industry, it’s international and inter-industry. You can find long whitepapers from all sorts of different governments laying out a quantum computing research strategy. There’s also a lot of venture capitalists investing in quantum computing.
Is this research and development public, or is there a lot of proprietary information out there? It’s a mixture, many of the startups and companies have software components that they are open sourcing and claim to have “bits of physics” working (quantum bits or qbits), but they are definitely keeping trade secrets.
19:50 Quantum communication means space lasers.
Engineering with quantum effects has promise as an industry. One can send photons with entangled states. The Chinese government has a satellite that can generate these photons and send them to base stations. If anyone reads them they can tell because the wave function collapsed too soon.
Quantum sensing promises to develop accelerometers and gyroscopes that are orders of magnitude more sensitive than what’s commercially available today.
Quantum engineering could become a new field. Much like electrical engineering was born 140 years ago, electronics was born roughly 70 years ago, computer science was born out of math and electrical engineering. It’s possible that the birth of quantum engineering will be considered to be some point in the next 5 years or last 5 years.
Lee’s favorite quantum state is the Bell state. It’s the equal probability state between 1 and 0, among other interesting properties. The Bell state encapsulates a lot of the quantum weirdness in one snippet of math.