Moore's law describes an important trend in the history of computer hardware. Since the invention of the integrated circuit in 1958, the number of transistors that can be placed inexpensively on an integrated circuit has increased exponentially, doubling approximately every two years. The trend was first observed by Intel co-founder Gordon E. Moore in a 1965 paper. It has continued for almost half of a century and is not expected to stop for another decade at least and perhaps much longer.
Almost every measure of the capabilities of digital electronic devices is linked to Moore's law: processing speed, memory capacity, even the number and size of pixels in digital cameras. All of these are improving at (roughly) exponential rates as well. This has dramatically increased the usefulness of digital electronics in nearly every segment of the world economy. Moore's law describes this driving force of technological and social change in the late 20th and early 21st centuries.
In 1975, Moore altered his projection to a doubling every two years. Despite popular misconception, he is adamant that he did not predict a doubling "every 18 months". However, an Intel colleague had factored in the increasing performance of transistors to conclude that integrated circuits would double in performance every 18 months.
In April 2005, Intel offered $10,000 to purchase a copy of the original Electronics Magazine. David Clark, an engineer living in the UK, was the first to find a copy and offer it to Intel.
Several measures of digital technology are improving at exponential rates related to Moore's law, including the size, cost, density and speed of components. Moore himself wrote only about the density of components (or transistors) at minimum cost. He noted: Transistors per integrated circuit. The most popular formulation is of the doubling of the number of transistors on integrated circuits every two years. At the end of the 1970s, Moore's law became known as the limit for the number of transistors on the most complex chips. Recent trends show that this rate has been maintained into 2007.
Density at minimum cost per transistor. This is the formulation given in Moore's 1965 paper. It is not about just the density of transistors that can be achieved, but about the density of transistors at which the cost per transistor is the lowest. As more transistors are put on a chip, the cost to make each transistor decreases, but the chance that the chip will not work due to a defect increases. In 1965, Moore examined the density of transistors at which cost is minimized, and observed that, as transistors were made smaller through advances in photolithography, this number would increase at "a rate of roughly a factor of two per year".
Cost per transistor. As the size of transistors has decreased, the cost per transistor has decreased as well. However, the manufacturing cost per unit area has only increased over time, since materials and energy expenditures per unit area have only increased with each successive technology node.
Computing performance per unit cost. Also, as the size of transistors shrinks, the speed at which they operate increases. It is also common to cite Moore's law to refer to the rapidly continuing advance in computing performance per unit cost, because increase in transistor count is also a rough measure of computer processing performance. On this basis, the performance of computers per unit cost—or more colloquially, "bang per buck"—doubles every 24 months.
Power consumption. the power consumption of compute nodes doubles every 18 months.
Hard disk storage cost per unit of information. A similar law (sometimes called Kryder's Law) has held for hard disk storage cost per unit of information. The rate of progression in disk storage over the past decades has actually sped up more than once, corresponding to the utilization of error correcting codes, the magnetoresistive effect and the giant magnetoresistive effect. The current rate of increase in hard drive capacity is roughly similar to the rate of increase in transistor count. Recent trends show that this rate has been maintained into 2007.
RAM storage capacity. Another version states that RAM storage capacity increases at the same rate as processing power.
Network capacity According to Gerry/Gerald Butters, the former head of Lucent's Optical Networking Group at Bell Labs, there is another version, called Butter's Law of Photonics, a formulation which deliberately parallels Moore's law. Butter's law says that the amount of data coming out of an optical fiber is doubling every nine months. Thus, the cost of transmitting a bit over an optical network decreases by half every nine months. The availability of wavelength-division multiplexing (sometimes called "WDM") increased the capacity that could be placed on a single fiber by as much as a factor of 100. Optical networking and DWDM is rapidly bringing down the cost of networking, and further progress seems assured. As a result, the wholesale price of data traffic collapsed in the dot-com bubble. Nielsen's Law says that the bandwidth available to users increases by 50% annually.
Pixels per dollar. Similarly, Barry Hendy of Kodak Australia has plotted the "pixels per dollar" as a basic measure of value for a digital camera, demonstrating the historical linearity (on a log scale) of this market and the opportunity to predict the future trend of digital camera price and resolution.
As the cost of computer power to the consumer falls, the cost for producers to fulfill Moore's law follows an opposite trend: R&D, manufacturing, and test costs have increased steadily with each new generation of chips. Rising manufacturing costs are an important consideration for the sustaining of Moore's law. This had led to the formulation of "Moore's second law," which is that the capital cost of a semiconductor fab also increases exponentially over time.
Materials required for advancing technology (e.g., photoresists and other polymers and industrial chemicals) are derived from natural resources such as petroleum and so are affected by the cost and supply of these resources. Nevertheless, photoresist costs are coming down through more efficient delivery, though shortage risks remain.
The cost to tape-out a chip at 90 nm is at least US$1,000,000, and exceeds US$3,000,000 for 65 nm.
Some of the new directions in research that may allow Moore's law to continue are:
While this time horizon for Moore's law scaling is possible, it does not come without underlying engineering challenges. One of the major challenges in integrated circuits that use nanoscale transistors is increase in parameter variation and leakage currents. As a result of variation and leakage, the design margins available to do predictive design are becoming harder. Such systems also dissipate considerable power even when not switching. Adaptive and statistical design along with leakage power reduction is critical to sustain scaling of CMOS. A good treatment of these topics is covered in Leakage in Nanometer CMOS Technologies Other scaling challenges include:
On April 13, 2005, Gordon Moore stated in an interview that the law cannot be sustained indefinitely: "It can't continue forever. The nature of exponentials is that you push them out and eventually disaster happens" and noted that transistors would eventually reach the limits of miniaturization at atomic levels:
In 1995, the "powerful" Digital Alpha 21164 chip had just over nine million transistors. This 64-bit processor was a technological spearhead at the time, even if the circuit’s market share remained average. Six years later, a state of the art microprocessor would have more than 40 million transistors. In 2015, it is believed that these processors should contain more than 15 billion transistors. Things are becoming smaller each year. If this continues, in theory, in less than 10 years computers will be created where each molecule will have its own place, i.e. we will have completely entered the era of molecular scale production.
Others see the limits of the law as being far in the distant future. Lawrence Krauss and Glenn D. Starkman announced an ultimate limit of around 600 years in their paper "Universal Limits of Computation", based on rigorous estimation of total information-processing capacity of any system in the Universe.
Then again, the law has often met obstacles that appeared insurmountable, before long surmounting them. In that sense, Moore says he now sees his law as more beautiful than he had realized: "Moore's law is a violation of Murphy's law. Everything gets better and better.
Extrapolation partly based on Moore's law has led futurists such as Vernor Vinge, Bruce Sterling, and Ray Kurzweil to speculate about a technological singularity. Kurzweil projects that a continuation of Moore's law until 2019 will result in transistor features just a few atoms in width. Although this means that the strategy of ever finer photolithography will have run its course, he speculates that this does not mean the end of Moore's law:
Thus, Kurzweil conjectures that it is likely that some new type of technology will replace current integrated-circuit technology, and that Moore's Law will hold true long after 2020. He believes that the exponential growth of Moore's law will continue beyond the use of integrated circuits into technologies that will lead to the technological singularity. The Law of Accelerating Returns described by Ray Kurzweil has in many ways altered the public's perception of Moore's Law. It is a common (but mistaken) belief that Moore's Law makes predictions regarding all forms of technology, when it actually only concerns semiconductor circuits. Many futurists still use the term "Moore's law" in this broader sense to describe ideas like those put forth by Kurzweil.
A sometimes misunderstood point is that exponentially improved hardware does not necessarily imply exponentially improved software performance to go with it. The productivity of software developers most assuredly does not increase exponentially with the improvement in hardware, but by most measures has increased only slowly and fitfully over the decades. Software tends to get larger and more complicated over time, and Wirth's law even states humorously that "Software gets slower faster than hardware gets faster".
There are problems where exponential increases in processing power are matched or exceeded by exponential increases in complexity as the problem size increases. (See computational complexity theory and complexity classes P and NP for a somewhat theoretical discussion of such problems, which occur very commonly in applications such as scheduling.)
Due to the mathematical power of exponential growth (similar to the financial power of compound interest), seemingly minor fluctuations in the relative growth rates of CPU performance, RAM capacity, and disk space per dollar have caused the relative costs of these three fundamental computing resources to shift markedly over the years, which in turn has caused significant changes in programming styles. For many programming problems, the developer has to decide on numerous time-space tradeoffs, and throughout the history of computing these choices have been strongly influenced by the shifting relative costs of CPU cycles versus storage space.
In addition to processor-usage/storage-space trade-offs, there is often a correlation between development time, application complexity, and application performance. One example of this would be the sorting algorithm insertion sort when compared to the quicksort algorithm. While an insertion sort is one of the easiest and least complex sorting algorithms to implement, it is also somewhat slow for large numbers of data. As processor performance increases, programmers may decide to implement slower and less complex algorithms in favor of a shorter development time.
Not all aspects of computing technology develop in capacities and speed according to Moore's law. Random Access Memory (RAM) speeds and hard drive seek times improve at best a few percentage points each year. Since the capacity of RAM and hard drives is increasing much faster than is their access speed, intelligent use of their capacity becomes more and more important. It now makes sense in many cases to trade space for time, such as by precomputing indexes and storing them in ways that facilitate rapid access, at the cost of using more disk and memory space: space is getting cheaper relative to time.
Moreover, there is a popular misconception that the clock speed of a processor determines its speed, also known as the Megahertz Myth. This actually also depends on the number of instructions per tick which can be executed (as well as the complexity of each instruction, see MIPS, RISC and CISC), and so the clock speed can only be used for comparison between two identical circuits. Of course, other factors must be taken into consideration such as the bus width and speed of the peripherals. Therefore, most popular evaluations of "computer speed" are inherently biased, without an understanding of the underlying technology. This was especially true during the Pentium era when popular manufacturers played with public perceptions of speed, focusing on advertising the clock rate of new products.
Another popular misconception circulating Moore's law is the incorrect assumption that exponential processor transistor growth, as predicted by Moore, translates directly into proportional exponential increase processing power or processing speed. While the increase of transistors in processors usually have an increased effect on processing power or speed, the relationship between the two factors is not proportional. There are cases where a ~45% increase in processor transistors have translated to roughly 10-20% increase in processing power or speed. Different processor families have different performance increases when transistor count is increased. More precisely, processor performance or power is more related to other factors such as microarchitecture, and clock speed within the same processor family. That is to say, processor performance can increase without increasing the number of transistors in a processor. (AMD64 processors had better overall performance compared to the late Pentium 4 series, which had more transistors).
It is also important to note that transistor density in multi-core CPUs does not necessarily reflect a similar increase in practical computing power, due to the unparallelised nature of most applications.