Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Akida Wu

dblp:83/2159 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 1997
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Memory systems · 56% Processor architecture and microarchitecture · 44%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Memory systems
cache
0.011997
Microarchitecture Support for Improving the Performance of Load Target Prediction · MICRO 1997
Memory systems › memory access latency
load latency
0.011997
Microarchitecture Support for Improving the Performance of Load Target Prediction · MICRO 1997
Processor architecture and microarchitecture
superscalar processor
0.011997
Microarchitecture Support for Improving the Performance of Load Target Prediction · MICRO 1997
Processor architecture and microarchitecture
instruction fetch
0.011997
Microarchitecture Support for Improving the Performance of Load Target Prediction · MICRO 1997
Processor architecture and microarchitecture
pipelining
0.011997
Microarchitecture Support for Improving the Performance of Load Target Prediction · MICRO 1997

Methods — techniques the papers use, named apart from their topics

load-prediction buffer · 0.0load stride prediction · 0.0bit-array design · 0.0
YearPublicationVenuePosition
1997 Microarchitecture Support for Improving the Performance of Load Target Prediction
abstract
Presents a load target prediction scheme that mitigates the impact of load latency for modern microprocessors. The scheme uses a cache-like buffer to provide the base address, offset and operand size at the instruction fetching stage of a pipeline so that a load target address can be computed earlier at the decode stage. With the dynamic use of a load stride, the scheme has achieved a prediction rate that is 15% higher than a previously proposed approach. By providing a 128-entry direct-mapped load-prediction buffer, two adders and two forwarding paths, for a 4-fetch processor the scheme provides an average speedup of 10% to 32% in performance improvement as the data cache latency increases from 2 cycles to 4 cycles. A bit-array design that supports multiple-cast writes and eliminates the associative logic commonly used in base register caching is developed for the prediction scheme.
Chung-Ho Chen, Akida Wu
MICRO2