Demo Data from EDBT 2026 / reviewers in the wild / expert
Anisha Saini
dblp:380/7186
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2026
0000-0001-6192-5232ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 since 2021Theory of computation · 1 · 1 since 2021
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 · 70% Hardware accelerators and domain-specific architectures · 30% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Processor architecture and microarchitecture › computer arithmetic
dot product |
1.0 | 1 | 2026 | Fused FP8 Many-Terms Dot Product With Scaling and FP32 Accumulation · IEEE Trans. Computers 2026 |
Processor architecture and microarchitecture › computer arithmetic
floating-point arithmetic |
1.0 | 1 | 2026 | Fused FP8 Many-Terms Dot Product With Scaling and FP32 Accumulation · IEEE Trans. Computers 2026 |
Hardware accelerators and domain-specific architectures › machine learning accelerator
low-precision arithmetic |
1.0 | 1 | 2026 | Fused FP8 Many-Terms Dot Product With Scaling and FP32 Accumulation · IEEE Trans. Computers 2026 |
Methods — techniques the papers use, named apart from their topics
scaling · 1.0fused many-terms dot product · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Fused FP8 Many-Terms Dot Product With Scaling and FP32 Accumulation
David Raymond Lutz, Anisha Saini, Mairin Kroes, Thomas Elmer, Harsha Valsaraju, Javier D. Bruguera |
IEEE Trans. Computers | 2 |
| 2024 | Fused FP8 4-Way Dot Product With Scaling and FP32 AccumulationabstractFor a variety of ML applications, generalized matrix multiply (GEMM) with DOT product is the most computationally intensive operation. This paper presents a microarchitecture exploration of fused multi-way reduced precision floating point multiply-accumulate with single rounding, resulting in power and area efficient characteristics. We propose two different microarchitectures, implementing novel design techniques for computing fused FP8 DOT4 accumulating to higher precision FP32 with scaling to adjust the dynamic range. Our first design, dot product with late accumulation, computes fused FP8 DOT4 by calculating the dot product in the first two cycles, expanding the products to fixed point format and another two cycles for the accumulation operation. This design allows the reuse of a slightly modified, FMA capable, FP32 adder. Our second design, dot product with early accumulation is implemented as a standalone FP8 datapath computing products and accumulation in first two cycles, and another two cycles for normalization and single rounding operation. This design aligns addends (products and accumulator) from an “Anchor” for efficient, arithmetically fused, N-way FP DOT product computation. Furthermore, we synthesized the two designs proposed in a 5nm technology node and compared the cost of implementation. David Raymond Lutz, Anisha Saini, Mairin Kroes, Thomas Elmer, Harsha Valsaraju |
ARITH | 2 |