, I can actually talk about the what was important about it. This paper is about power, so here are the important parts:
The summary is that data-flow processing will have stagnant power improvements from scaling, memory transfers are of fixed power, and cache is really not helping as much as you think when it comes to relative performance per Watt.
The Multiply ACcumulate (MAC) is a mathematical calculation that computers spend a good deal of their time doing. It's used heavily in natural signal processing, such as audio, and video cards have GPUs that designed to quickly do these calculations. There are two players in the marketing of the semiconductor space who have "Law's" that are not actual laws, but more of trends that have been noticed: Moore and Koomey
. Moore said that more transistors will fit on a die, and Koomey did a survey that showed that power consumption is still scaling. The assumption is that as transistors have scaled, their relative behavior has scaled, which is not true. Another item that comes into to play on the marketing side is how you measure power. Most processors offload some power requirements to RAM that has sense amps just burning current to have an effective input capacitance, which makes the CPU power lower at the cost of increased RAM power. If you really want to get a good feel for the power requirements of a system the Giga-MAC per Watt (GMAC/W) is a great calculation.
The the graph above, I present Koomey's system trend and then the trend specific to the GMAC/W . Koomey included systems until 2006, but did not focus on specifically on data flow applications. If you are trying to do things like audio, video games, watching the sky, processing speech, RADAR, and just about any natural system, you are burdened by how much data you can move through the system, and how to get the heat out and data and power in. I am confident that for digital approaches there exists a Powerwall at between 10 to 30 GMAC/W. ie: the end of useful scaling for power constrained systems.
When I first started looking at this with Dr. Marr, it was because of a program called
. The question was if the specs were actually achievable with classical digital approaches, and I happen to play in the space of non-classical digital and analog processing. The idea of ACT is to build a phased array system where the amount of bandwidth and number of beams that can be formed scales per unit Watt. This would allow powerful phased array technology to proliferate in many types of devices, not just expensive military radars. Also, the cloud computing revolution, in part, depends on Koomey's law continuing; if we are to cloud compute with mobile devices, we must be able to transmit, receive, and process wireless data for ever smaller energy levels assuming fixed or slowly growing energy density in batteries, which so far has been the case.
Conclusion: DARPA ACT will not hit their power target with classical digital approaches.
I have not yet seen the final reports, but I hope the teams prove me wrong. I doubt it.
Memory transfer power halves every 29 years, which makes memory power a fixed commodity calculation for data-flow systems. If you have data that is constantly changing, you are burdened by the speed and power of the data bus.
When it comes to memory, it really depends on architecture. As far as improvements, it's life in the slow lane.
In a non-dataflow system, cache is king. If you cannot get data in faster, you need to keep it local and handy. I looked into what SPECInt2006 had to say about this using the
. If data transfer power is constrained by IO, you need have a lot of cache to minimize non-data instructions coming into the system, but what about power? Using the data in the CPUDB, I plotted SPECInt2006 basemean score per Watt, and then normalized it for processor cache.
I believe that these improvements in SPECint96 score are based upon increases in processor cache because the graph shows almost no trend when the cache is normalized across the processors. As data flow applications are not affected by substantial increase in cache sizes, I believe that there is little improvement between years due to scaling regarding score per Watt.
However, it does make a good argument for simpler code. When you consider that how large programs have gotten relative to cache, the Computer Science community should consider trimming the fat a bit.
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