Demo Data from SIGIR 2026 / reviewers in the wild / expert
Shay Beren
dblp:30/5355
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2003
—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 |
Embedded and real-time systems · 61% Processor architecture and microarchitecture · 30% Electronic design automation · 9% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Embedded and real-time systems › embedded processor
code compression |
0.0 | 1 | 2003 | Class-Based Decompressor Design for Compressed Instruction Memory in Embedded Processors · IEEE Trans. Computers 2003 |
Embedded and real-time systems
embedded processor |
0.0 | 1 | 2003 | Class-Based Decompressor Design for Compressed Instruction Memory in Embedded Processors · IEEE Trans. Computers 2003 |
Processor architecture and microarchitecture
instruction set architecture |
0.0 | 1 | 2003 | Class-Based Decompressor Design for Compressed Instruction Memory in Embedded Processors · IEEE Trans. Computers 2003 |
Electronic design automation
logic synthesis |
0.0 | 1 | 2003 | Class-Based Decompressor Design for Compressed Instruction Memory in Embedded Processors · IEEE Trans. Computers 2003 |
Methods — techniques the papers use, named apart from their topics
class-based coding · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2003 | Class-Based Decompressor Design for Compressed Instruction Memory in Embedded ProcessorsabstractWe introduce a new PLA-based decoder architecture for random-access runtime decompression of compressed instruction memory in embedded systems. The compression method employs class-based coding. The symbol codebook used for decompression is fully programmable; thus, good compression may be achieved by adapting the codebook to the symbol frequency statistics of the target binary program. We show that this new class-based decoder architecture can be extended to provide high throughput decompression. Shlomo Weiss, Shay Beren |
IEEE Trans. Computers | 2 |