Demo Data from EDBT 2026 / reviewers in the wild / expert
Albert Danysh
dblp:42/3649
· DBLP profile ↗
2ranked-venue papers
1as first author
0since 2021 · last 2005
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 first-authorTheory of computation · 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 |
Processor architecture and microarchitecture · 77% Integrated circuit design · 12% Reconfigurable computing and FPGAs · 12% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Processor architecture and microarchitecture
arithmetic unit |
0.1 | 1 | 2005 | Architecture and Implementation of a Vector/SIMD Multiply-Accumulate Unit · IEEE Trans. Computers 2005 |
Processor architecture and microarchitecture › arithmetic unit
multiply-accumulate unit |
0.1 | 1 | 2005 | Architecture and Implementation of a Vector/SIMD Multiply-Accumulate Unit · IEEE Trans. Computers 2005 |
Integrated circuit design
low-power circuit design |
0.0 | 1 | 2005 | Architecture and Implementation of a Vector/SIMD Multiply-Accumulate Unit · IEEE Trans. Computers 2005 |
Reconfigurable computing and FPGAs › dynamic reconfiguration
runtime reconfigurable processor |
0.0 | 1 | 2005 | Architecture and Implementation of a Vector/SIMD Multiply-Accumulate Unit · IEEE Trans. Computers 2005 |
Methods — techniques the papers use, named apart from their topics
shared subtree · 0.1shared segmentation · 0.1partial product generation · 0.1carry chain kill · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2005 | Architecture and Implementation of a Vector/SIMD Multiply-Accumulate UnitabstractThis work presents 64-bit fixed-point vector multiply-accumulator (MAC) architecture capable of supporting multiple precisions. The vector MAC can perform one 64/spl times/64, two 32/spl times/32, four 16/spl times/16, or eight 8/spl times/8 bit signed/unsigned multiply using essentially the same hardware as a scalar 64-bit MAC and with only a small increase in delay. The scalar MAC architecture is "vectorized" by inserting mode-dependent multiplexing into the partial product generation and by inserting mode-dependent kills in the carry chain of the reduction tree and the final carry-propagate adder. This is an example of "shared segmentation" in which the existing scalar structure is segmented and then shared between vector modes. The vector MAC is area efficient and can be fully pipelined, which makes it suitable for high-performance processors and, possibly, dynamically reconfigurable processors. The "shared segmentation" method is compared to an alternative method, referred to as the "shared subtree" method, by implementing vector MAC designs using two different technologies and three different vector widths. Albert Danysh, Dimitri Tan |
IEEE Trans. Computers | 1 |
| 2003 | Multiple-Precision Fixed-Point Vector Multiply-Accumulator Using Shared SegmentationabstractWe present a 64-bit fixed-point vector multiply-accumulator (MAC) architecture capable of supporting multiple precisions. The vector MAC can perform one 64/spl times/64, two 32/spl times/32, four 16/spl times/16 or eight 8/spl times/8 bit signed/unsigned multiply-accumulates using essentially the same hardware as a scalar 64-bit MAC and with only a small increase in delay. The scalar MAC architecture is "vectorized" by inserting mode-dependent multiplexing into the partial product generation and by inserting mode-dependent kills in the carry chain of the reduction tree and the final carry-propagate adder. This is an example of "shared segmentation" in which the existing scalar structure is segmented and then shared between vector modes. The vector MAC is area efficient and can be fully pipelined which makes it suitable for high-performance processors and possibly dynamically reconfigurable processors. Dimitri Tan, Albert Danysh, Michael J. Liebelt |
IEEE Symposium on Computer Arithmetic | 2 |