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Computers that control machinery usually need low interrupt latencies. These computers operate in a real-time environment and fail if an operation is not completed in a specified amount of time. For example, computer-controlled anti-lock brakes must begin braking within a predictable, short time after the brake pedal is sensed or else failure of the brake will occur.
Benchmarking takes all these factors into account by measuring the time a computer takes to run through a series of test programs. Although benchmarking shows strengths, it shouldn't be how you choose a computer. Often the measured machines split on different measures. For example, one system might handle scientific applications quickly, while another might render video games more smoothly. Furthermore, designers may target and add special features to their products, through hardware or software, that permit a specific benchmark to execute quickly but don't offer similar advantages to general tasks.
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Power efficiency is another important measurement in modern computers. A higher power efficiency can often be traded for lower speed or higher cost. Modern circuits have less power required per transistor as the number of transistors per chip grows.
However the number of transistors per chip is starting to increase at a slower rate. Therefore, power efficiency is starting to become as important, if not more important than fitting more and more transistors into a single chip.
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Recent processor designs have shown this emphasis as they put more focus on power efficiency rather than cramming as many transistors into a single chip as possible. Increases in clock frequency have grown more slowly over the past few years, compared to power reduction improvements. This has been driven by the end of Moore's Law and demand for longer battery life and reductions in size for mobile technology.
From Wikipedia, the free encyclopedia. Set of rules and methods that describe the functionality, organization, and implementation of computer systems. This section does not cite any sources. Please help improve this section by adding citations to reliable sources. Unsourced material may be challenged and removed. March Learn how and when to remove this template message. Main article: Instruction set architecture. Main article: Microarchitecture. Main article: Implementation. Main article: Low-power electronics. Electronics portal. Principles of Computer Hardware Fourth ed. Architecture describes the internal organization of a computer in an abstract way; that is, it defines the capabilities of the computer and its programming model.
You can have two computers that have been constructed in different ways with different technologies but with the same architecture. This task has many aspects, including instruction set design, functional organization, logic design,and implementation. Copeland Ed. Retrieved 7 October Planning a Computer System. Retrieved 11 May Geschichten der Informatik: Visionen, Paradigmen, Leitmotive.
Hennessy and David A. Morgan Kaufmann Publishers. Dictionary of Computer Science, Engineering, and Technology. CRC Press. Retrieved 8 May April Retrieved 5 May Computer science. Computer architecture Embedded system Real-time computing Dependability.
Addressing Modes? Trace cache, KB L2 cache. Assume that idle energy is negligible.
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Synthetic benchmarks predict performance for real programs. Infrastructure providers now offer. Gheith Abandah Adapted from the slides of Prof. There are dozens of active research areas but I'll touch on a few that are the most impactful in my opinion. A growing trend that we've been impacted by is heterogeneous computing.
This is the method of including multiple different computing elements in a single system. Most of us benefit from this in the form of a dedicated GPU in our systems. A CPU is very customizable and can perform a wide variety of computations at a reasonable speed. A GPU, on the other hand, is designed specifically to perform graphics calculations like matrix multiplication.get link
IEEE Computer Society Predicts the Future of Tech: Top 10 Technology Trends for 12222
It is really good at that and is orders of magnitude faster than a CPU at those types of instructions. It's easy for any programmer to optimize software by tweaking an algorithm, but optimizing hardware is much more difficult.
But GPUs aren't the only area where accelerators are becoming common. Most smartphones have dozens of hardware accelerators designed to speed up very specific tasks. As workloads get more and more specialized, hardware designers are including more and more accelerators into their chips. It's almost like programmable hardware that can be configured to whatever your computing needs are. If you want to do image recognition, you can implement those algorithms in hardware. If you want to simulate how a new hardware design will perform, you can test it on the FPGA before actually building it.
Other companies like Google and Nvidia are developing dedicated machine learning ASICs to speed up image recognition and analysis. Looking at the die shots of some fairly recent processors, we can see that most of the area of the CPU is not actually the core itself. A growing amount is being taken up by accelerators of all different types. This has helped speeding up very specialized workloads in addition to the benefit of huge power savings.
Historically, if you wanted to add video processing to a system, you'd just add a chip to do it. That is hugely inefficient though. Every time a signal has to go out of a chip on a physical wire to another chip, there is a large amount of energy required per bit. On its own, a tiny fraction of a Joule may not seem like a lot, but it can be orders of magnitude more efficient to communicate within the same chip versus going off chip. We have seen the growth of ultra low power chips thanks to the integration of these accelerators into the CPUs themselves.
Accelerators aren't perfect though. As we add more of them to our designs, chips become less flexible and start to sacrifice overall performance for peak performance in certain workloads. At some point, the whole chip just becomes a collection of accelerators and then it isn't a useful CPU anymore. The tradeoff between specialized performance and general performance is always being fine tuned.
This disconnect between generalized hardware and specific workloads is known as the specialization gap. As the cloud and AI continue to grow, GPUs appear to be our best solution so far to achieve the massive amounts of compute needed. Another area where designers are looking for more performance is memory. Traditionally, reading and writing values has been one of the biggest bottlenecks for processors.
Because of this, engineers often view memory access as more expensive than the actual computation itself. If your processor wants to add two numbers, it first needs to calculate the memory addresses where the numbers are stored, find out what level of the memory hierarchy has the data, read the data into registers, perform the computation, calculate the address of the destination, and write back the value to wherever it is needed. For simple instructions that may only take a cycle or two to complete, this is extremely inefficient.
A novel idea that has seen a lot of research is a technique called Near Memory Computing. Rather than fetching small bits of data from memory to bring to the fast processor for compute, researchers are flipping this idea around. By doing the computation closer to the memory, there is the potential for huge energy and time savings since data doesn't need to be transferred around as much.