Computers using the tagged-token dataflow model are among the best candidates
for delivering extremely high levels of performance required in the future. Instruction
scheduling in these computers is determined by associatively matching data-bearing
tokens in a Waiting-Matching Unit (W-M unit). At the W-M unit, incoming tokens
with matching contexts are forwarded to an instruction while non-matching tokens are
stored to await their matching partner.
Requirements of the W-M unit are exacting. Necessary token storage capacity
at each processing element (PE) is presently estimated to be 100,000 tokens. Since the
most often executed arithmetic instructions require two operands, the bandwidth of the
W-M unit must be approximately twice that of the ALU. The contradictory
requirements of high storage capacity and high memory bandwidth have compromised
the M-W units of previous dataflow computers limiting their speed.
However, tokens arriving at a PE exhibit strong temporal locality. This
naturally suggests the use of some caching technique. Using a recently developed
CAM memory structure as a base, a token caching scheme is described which allows
rapid, fully associative token matching while allowing a large token storage capacity.
The key to the caching scheme is a fast and compact, articulated, first-in, first-out,
content addressable memory (AFCAM) which allows associative matching and
garbage collection while maintaining temporal ordering. A new memory cell is
developed as the basis for the AFCAM in an advanced CMOS (Complementary Metal
Oxide Semiconductor) technology. The design of the cell is discussed as well as
electrical simulation results, verifying its operation and performance.
Finally, estimated system performance of a dataflow computer using the
caching scheme is presented. / Graduation date: 1991
| Identifer | oai:union.ndltd.org:ORGSU/oai:ir.library.oregonstate.edu:1957/37538 |
| Date | 03 May 1991 |
| Creators | Traylor, Roger L. |
| Contributors | Bose, Bella |
| Source Sets | Oregon State University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
Page generated in 0.0022 seconds