Computational RAM

Computational RAM or C-RAM is random-access memory with processing elements integrated on the same chip. This enables C-RAM to be used as a SIMD computer. It also can be used to more efficiently use memory bandwidth within a memory chip.

Perhaps the most influential implementations of computational RAM came from The Berkeley IRAM Project. Vector IRAM (V-IRAM) combines DRAM with a vector processor integrated on the same chip.[1]

Reconfigurable Architecture DRAM (RADram) is DRAM with reconfigurable computing FPGA logic elements integrated on the same chip.[2] SimpleScalar simulations show that RADram (in a system with a conventional processor) can give orders of magnitude better performance on some problems than traditional DRAM (in a system with the same processor).

Some embarrassingly parallel computational problems are already limited by the von Neumann bottleneck between the CPU and the DRAM. Some researchers expect that, for the same total cost, a machine built from computational RAM will run orders of magnitude faster than a traditional general-purpose computer on these kinds of problems.[3]

As of 2011, the "DRAM process" (few layers; optimized for high capacitance) and the "CPU process" (optimized for high frequency; typically twice as many BEOL layers as DRAM; since each additional layer reduces yield and increases manufacturing cost, such chips are relatively expensive per square millimeter compared to DRAM) is distinct enough that there are three approaches to computational RAM:

Some CPUs designed to be built on a DRAM process technology (rather than a "CPU" or "logic" process technology specifically optimized for CPUs) include The Berkeley IRAM Project, TOMI Technology[4][5] and the AT&T DSP1.

Because a memory bus to off-chip memory has many times the capacitance of an on-chip memory bus, a system with separate DRAM and CPU chips can have several times the energy consumption of an IRAM system with the same computer performance. [1]

Because computational DRAM is expected to run hotter than traditional DRAM, and increased chip temperatures result in faster charge leakage from the DRAM storage cells, computational DRAM is expected to require more frequent DRAM refresh. [2]


Processor-in-memory

A processor-in-memory (PIM) refers to a computer processor (CPU) tightly coupled to memory, generally on the same silicon chip.

The chief goal of merging the processing and memory components in this way is to reduce memory latency and increase bandwidth. Alternatively reducing the distance that data needs to be moved reduces the power requirements of a system. Much of the complexity (and hence power consumption) in current processors stems from strategies to deal with avoiding memory stalls.

Examples

In the 1980s, a tiny CPU that executed FORTH was fabricated into a DRAM chip to improve PUSH and POP. FORTH is a Stack-oriented programming language and this improved its efficiency.

The Transputer also had large on chip memory given that it was made in the early 1980s making it essentially a processor-in-memory.

Notable PIM projects include the Berkeley IRAM project (IRAM) at the University of California, Berkeley[6] project or the University of Notre Dame PIM[7] effort.

See also

References

  1. 1 2 3 Christoforos E. Kozyrakis, Stylianos Perissakis, David Patterson, Thomas Anderson, et al. "Scalable Processors in the Billion-Transistor Era: IRAM". IEEE Computer (magazine). 1997. says "Vector IRAM ... can operate as a parallel built-in self-test engine for the memory array, significantly reducing the DRAM testing time and the associated cost."
  2. 1 2 Mark Oskin, Frederic T. Chong, and Timothy Sherwood. "Active Pages: A Computation Model for Intelligent Memory". 1998.
  3. Daniel J. Bernstein. "Historical notes on mesh routing in NFS". 2002. "programming a computational RAM"
  4. "TOMI the milliwatt microprocessor"
  5. Yong-Bin Kim and Tom W. Chen. "Assessing Merged DRAM/Logic Technology". 1998.
  6. IRAM
  7. PIM

Bibliography

This article is issued from Wikipedia - version of the 5/26/2015. The text is available under the Creative Commons Attribution/Share Alike but additional terms may apply for the media files.