Demo Data from SIGIR 2026 / reviewers in the wild / expert
Christopher Han-Yu Chou
dblp:40/7074
· DBLP profile ↗
2ranked-venue papers
1as first author
0since 2021 · last 2011
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 2 · 1 first-author
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 |
Reconfigurable computing and FPGAs · 39% Memory systems · 30% Processor architecture and microarchitecture · 30% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Memory systems › on-chip memory
scratchpad memory |
0.1 | 1 | 2011 | VEGAS: soft vector processor with scratchpad memory · FPGA 2011 |
Reconfigurable computing and FPGAs › reconfigurable architecture › reconfigurable processor
soft vector processor |
0.1 | 1 | 2011 | VEGAS: soft vector processor with scratchpad memory · FPGA 2011 |
Processor architecture and microarchitecture
vector processor |
0.1 | 1 | 2011 | VEGAS: soft vector processor with scratchpad memory · FPGA 2011 |
Reconfigurable computing and FPGAs › FPGA-based processor implementation
soft-core processor |
0.0 | 1 | 2011 | VEGAS: soft vector processor with scratchpad memory · FPGA 2011 |
Methods — techniques the papers use, named apart from their topics
C macro API · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2011 | VEGAS: soft vector processor with scratchpad memoryabstractThis paper presents VEGAS, a new soft vector architecture, in which the vector processor reads and writes directly to a scratchpad memory instead of a vector register file. The scratchpad memory is a more efficient storage medium than a vector register file, allowing up to 9x more data elements to fit into on-chip memory. In addition, the use of fracturable ALUs in VEGAS allow efficient processing of bytes, halfwords and words in the same processor instance, providing up to 4x the operations compared to existing fixed-width soft vector ALUs. Benchmarks show the new VEGAS architecture is 10x to 208x faster than Nios II and has 1.7x to 3.1x better area-delay product than previous vector work, achieving much higher throughput per unit area. To put this performance in perspective, VEGAS is faster than a leading-edge Intel processor at integer matrix multiply. To ease programming effort and provide full debug support, VEGAS uses a C macro API that outputs vector instructions as standard NIOS II/f custom instructions. Christopher Han-Yu Chou, Aaron Severance, Alex D. Brant, Zhiduo Liu, Saurabh Sant, Guy Lemieux |
FPGA | 1 |
| 2009 | Vector Processing as a Soft Processor AcceleratorabstractCurrent FPGA soft processor systems use dedicated hardware modules or accelerators to speed up data-parallel applications. This work explores an alternative approach of using a soft vector processor as a general-purpose accelerator. The approach has the benefits of a purely software-oriented development model, a fixed ISA allowing parallel software and hardware development, a single accelerator that can accelerate multiple applications, and scalable performance from the same source code. With no hardware design experience needed, a software programmer can make area-versus-performance trade-offs by scaling the number of functional units and register file bandwidth with a single parameter. A soft vector processor can be further customized by a number of secondary parameters to add or remove features for a specific application to optimize resource utilization. This article introduces VIPERS, a soft vector processor architecture that maps efficiently into an FPGA and provides a scalable amount of performance for a reasonable amount of area. Compared to a Nios II/s processor, instances of VIPERS with 32 processing lanes achieve up to 44× speedup using up to 26× the area. Jason Yu, Christopher Eagleston, Christopher Han-Yu Chou, Maxime Perreault, Guy Lemieux |
ACM Trans. Reconfigurable Technol. Syst. | 3 |