Doi: 10.1145/1785414.1785440

technical Perspective
technology scaling
Redirects main memories
By Mary Jane Irwin

as prediCTed b Y Intel’s Gordon Moore in 1965, based on his observation of the scaling of several generations of silicon technology at the time, the number of transistors that can be integrated on one die continues to double approximately every two years. Amazing to some, Moore’s Law has prevailed for 45 years and is expected to continue for several more generations. Transistor feature size and die integration capacity projections from the International Technology Roadmap for Semiconductors (ITRS) roadmap is shown in the accompanying table here.

These faster and more abundant transistors have been exploited by computer engineers to build processors that double in performance about every two years. Up until the beginning of this decade, that was done through faster clock speeds and clever architectural enhancements. Many of these architectural enhancements were directed at tackling the “memory wall,” which still plagues us today. Early in this decade, we ran into the “power wall” that dramatically slowed the increase in clock speeds. Since then, we are still seeing performance doublea every two years, but now it’s through having more cores (running at only modestly faster clock rates) on one die since technology scaling provides all of those additional transistors.

Another key component on the motherboard affected by technology scaling is the main memory, traditionally built out of dynamic random access memory (DRAM) parts. DRAMs have been doubling in capacity every two to three years while their access latency has improved about 7% per year. However, processors speeds still leave main memories in the dust—with the processors having to wait 100 or more cycles to get information back from

Projections for transistor size and die integration capacity.

Year Feature size (nm) integration Capacity (bt)

2004

90

2

2006

65

4

2008

45

6

2010

32

16

2012

22

32

 

a But one only really gets double the performance if they can figure out how to keep all of those cores busy.

main memory—hence, the focus by architects on cache memory systems that tackle this “memory wall.” And multicore parts put even more pressure on the DRAM, demanding more capacity, lower latencies, and better bandwidth.

As pointed out in the following paper by Lee, Ipek, Mutlu, and Burger, DRAM memory scaling is in jeopardy, primarily due to reliability issues. The storage mechanism in DRAMs, charge storage and maintenance in a capacitor, requires inherently unscalable charge placement and control. Flash memories, which have the advantage of being nonvolatile, have their own scaling limitations. Thus, the search for new main memory technologies has begun.

The authors make a case for phase change memories (PCMs) that are nonvolatile and can scale below 40nm. PCMs store state by forcing a phase change in their storage element (for example, chalcogenide) to a high resistance state (so storing a “0”) or to a low resistance state (so storing a “ 1”). Fortunately, programming current scales linearly. However, PCMs do not come without their disadvantages: read and, especially, write latencies several times slower than DRAMs, write energies several times larger than DRAMs, and, like Flash, a limited lifetime directly related to the number of writes to a memory location.

This paper is a wonderful illustra-
tion of the way computer architects
can work around the limitations of the
technology with clever architectural
enhancements—turning lemons into
lemonade. By using an area-neutral
memory buffer reorganization, the
authors are able to reduce application
execution time from 1.6X to only 1.2X
relative to a DRAM-based system and
memory array energy from 2.2X to 1.0X
also relative to a DRAM-based system.
They use multiple, narrower memory
buffers, which reduces the number of
expensive (in terms of both area and
power) sense amplifiers and focus on
application performance rather than
the performance of an individual
memory cell.

Mary Jane Irwin ( [e-mail]) is evan Pugh Professor and A. Robert noll Chair in engineering in the Department of Computer science and engineering at Penn state university, university Park, PA.