Moore's Law, which has been used by the semiconductor industry as a predictive indicator of its growth, is now widely believed to be faltering. However, a recent paper proposes a quantitative modification to Moore's Law that can more accurately predict the roadmap of chip performance and energy consumption. The paper argues that transistor density grows at a sub-exponential rate and so does performance. Before 2005, mainstream CPUs were almost all single cores, but now wearable devices easily package eight cores. In the future, we will definitely see more cores and require parallel algorithms and new programming paradigms. To achieve higher speedup, optimizing the processor architecture as a whole is necessary. Jim Keller's experience with X86 architecture suggests that microarchitecture should be redesigned every 3-5 years. The study also highlights that memory is almost two orders of magnitude slower than processors, and data movement among different processors will make this problem worse as the number of processors grows. Various new DSA architectures are now designed to alleviate such problems; however, this leads to intricately multitude software. Furthermore, derivative laws of Moore's Law such as Koomey's Law must be revised as well with this modification in mind. Koomey's Law states that for the same computing power, required energy consumption will be halved every certain time period or conversely computing power that can consume the same energy will be doubled every certain time period. Recent data shows that nearly half of algorithms have had no improvements while only 14% have improved beyond Moore's Law. In conclusion, while Moore’s Law may not hold up in its original form anymore due to sub-exponential growth rates in transistor density and performance gains, it can still serve as an important benchmark for predicting chip performance and energy consumption with some modifications. Additionally, derivative laws like Koomey’s Law need revision based on these modifications too.
Moore's Law is a rule that says computer chips get better and faster over time, but it's not working as well as it used to. Scientists are trying to come up with new ways to make chips work better. Chips used to have one brain (core), but now they have many brains (cores). This means we need new ways of telling them what to do. The memory in computers is slower than the brains, which can cause problems.
Definitions
- Moore's Law: A prediction made by Gordon Moore in 1965 that the number of transistors on a computer chip would double every two years, leading to faster and more powerful computers.
- Transistor density: The number of transistors that can be placed on a computer chip.
- Performance gains: Improvements in how fast and efficiently a computer chip can process information.
- CPUs: Central Processing Units, the "brain" of a computer that performs most of its processing tasks.
- Speedup: The increase in performance achieved by making a system or component faster.
- Memory: The part of a computer where data is stored for short-term use by the CPU.
- DSA architectures: Data Storage Architectures designed to improve data movement among different processors.
- Koomey's Law: A prediction made by Jonathan Koomey in 2011 that energy efficiency for computing would double every 18 months.
Moore's Law Faltering: A Quantitative Modification to Predict Chip Performance and Energy Consumption
For decades, Moore’s Law has been used by the semiconductor industry as a predictive indicator of its growth. However, in recent years it is becoming increasingly evident that Moore’s Law is faltering. This has led researchers to propose a quantitative modification to Moore’s Law that can more accurately predict the roadmap of chip performance and energy consumption.
Transistor Density Growth at Sub-Exponential Rate
The paper argues that transistor density grows at a sub-exponential rate and so does performance. Before 2005, mainstream CPUs were almost all single cores but now wearable devices easily package eight cores or more. In the future, we will definitely see an increase in core numbers which will require parallel algorithms and new programming paradigms for higher speedup optimization of processor architecture as a whole. Jim Keller's experience with X86 architecture suggests that microarchitecture should be redesigned every 3-5 years for better performance gains.
Memory Slower Than Processors
The study also highlights that memory is almost two orders of magnitude slower than processors; this problem gets worse when data needs to be moved among different processors due to increasing number of cores. To address this issue various new DSA architectures are being designed; however, this leads to intricately multitude software development challenges too.
Koomey's Law Needs Revision Too
Derivative laws of Moore’s Law such as Koomey’s Law must also be revised with this modification in mind. Koomey’s law states that for the same computing power required energy consumption will be halved every certain time period or conversely computing power that can consume the same energy will be doubled every certain time period. Recent data shows nearly half of algorithms have had no improvements while only 14% have improved beyond Moore's law expectations making it necessary to revise these derivative laws too based on these modifications suggested by researchers recently .
Conclusion
In conclusion, while Moore’s law may not hold up in its original form anymore due to sub-exponential growth rates in transistor density and performance gains, it can still serve as an important benchmark for predicting chip performance and energy consumption with some modifications made accordingly . Additionally , derivative laws like Koomey’s law need revision based on these modifications too .
Moore's Law has been used by semiconductor industry as predicative indicators of the industry and it has become a self-fulfilling prophecy. Now more people tend to agree that the original Moore's Law started to falter. This paper proposes a possible quantitative modification to Moore's Law. It can cover other derivative laws of Moore's Law as well. It intends to more accurately predict the roadmap of chip's performance and energy consumption.
