EDBT 2026 Demo / reviewers in the wild / expert
Mohammad Sepehr Pourghannad
dblp:338/6343
· DBLP profile ↗
3ranked-venue papers
0as first author
3since 2021 · last 2024
0009-0002-7807-1178ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 3 · 3 since 2021Software engineering, systems software and programming languages · 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
3 papers |
Electronic design automation · 36% Reconfigurable computing and FPGAs · 20% Hardware accelerators and domain-specific architectures · 13% | |
| Databases, data mining, and information retrieval
1 paper |
Machine learning and data management · 50% Data integration and cleaning · 50% |
Topics — the 11 heaviest of 12, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Electronic design automation › hardware verification and test
hardware verification |
0.9 | 2 | 2024 | Manticore: Hardware-Accelerated RTL Simulation with Static Bulk-Synchronous Parallelism · ASPLOS (4) 2023 A 475 MHz Manycore FPGA Accelerator for RTL Simulation · FPGA 2024 |
Electronic design automation › hardware verification and test › hardware verification
RTL simulation |
0.9 | 2 | 2024 | Manticore: Hardware-Accelerated RTL Simulation with Static Bulk-Synchronous Parallelism · ASPLOS (4) 2023 A 475 MHz Manycore FPGA Accelerator for RTL Simulation · FPGA 2024 |
Machine learning and data management
data management for machine learning |
0.8 | 1 | 2024 | Pecan: Cost-Efficient ML Data Preprocessing with Automatic Transformation Ordering and Hybrid Placement · USENIX ATC 2024 |
Data integration and cleaning
data preprocessing |
0.8 | 1 | 2024 | Pecan: Cost-Efficient ML Data Preprocessing with Automatic Transformation Ordering and Hybrid Placement · USENIX ATC 2024 |
Parallel and multicore computing › parallel computation models
bulk synchronous parallel |
0.7 | 1 | 2023 | Manticore: Hardware-Accelerated RTL Simulation with Static Bulk-Synchronous Parallelism · ASPLOS (4) 2023 |
Hardware accelerators and domain-specific architectures › scientific computing accelerator
simulation accelerator |
0.7 | 1 | 2023 | Manticore: Hardware-Accelerated RTL Simulation with Static Bulk-Synchronous Parallelism · ASPLOS (4) 2023 |
Cloud and datacenter computing
cluster resource management and scheduling |
0.2 | 1 | 2024 | Pecan: Cost-Efficient ML Data Preprocessing with Automatic Transformation Ordering and Hybrid Placement · USENIX ATC 2024 |
Processor architecture and microarchitecture
many-core architecture |
0.2 | 1 | 2024 | A 475 MHz Manycore FPGA Accelerator for RTL Simulation · FPGA 2024 |
Reconfigurable computing and FPGAs › FPGA-based processor implementation
soft-core processor |
0.2 | 1 | 2024 | A 475 MHz Manycore FPGA Accelerator for RTL Simulation · FPGA 2024 |
Performance modeling and evaluation › simulation › architectural simulation
cycle-accurate simulation |
0.2 | 1 | 2023 | Manticore: Hardware-Accelerated RTL Simulation with Static Bulk-Synchronous Parallelism · ASPLOS (4) 2023 |
Integrated circuit design › digital system design
register-transfer level design |
0.2 | 1 | 2023 | Manticore: Hardware-Accelerated RTL Simulation with Static Bulk-Synchronous Parallelism · ASPLOS (4) 2023 |
Methods — techniques the papers use, named apart from their topics
hybrid placement · 1.5automatic transformation ordering · 1.5static bulk-synchronous parallelism · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | A 475 MHz Manycore FPGA Accelerator for RTL SimulationabstractThis paper presents the implementation of Manticore: a manycore accelerator for parallel RTL simulation. Manticore packs up to 225 custom soft processors running at 475 MHz on a large FPGA. Implementing manycore accelerators on FPGAs is challenging as designers must reconcile the conflicting goals of maximizing the number of cores on the chip and clocking them at the highest possible frequency. Designers face two classes of constraints: (1) architectural constraints imposed by a large FPGA's multi-die structure, and (2) physical constraints imposed by the FPGA shell's size and placement. Physical design therefore plays a critical role in the implementation of manycore accelerators. We present physical design challenges faced during Manticore's implementation on the AMD Alveo U200 card---a large FPGA with a poorly-placed, wide shell that challenges physical implementation. Sahand Kashani, Mahyar Emami, Keisuke Kamahori, Mohammad Sepehr Pourghannad, Ritik Raj, James R. Larus |
FPGA | 4 |
| 2024 | Pecan: Cost-Efficient ML Data Preprocessing with Automatic Transformation Ordering and Hybrid Placement
Dan Graur, Oto Mraz, Muyu Li, Mohammad Sepehr Pourghannad, Chandramohan A. Thekkath, Ana Klimovic |
USENIX ATC | 4 |
| 2023 | Manticore: Hardware-Accelerated RTL Simulation with Static Bulk-Synchronous ParallelismabstractThe demise of Moore's Law and Dennard Scaling has revived interest in specialized computer architectures and accelerators. Verification and testing of this hardware depend heavily upon cycle-accurate simulation of register-transfer-level (RTL) designs. The fastest software RTL simulators can simulate designs at 1--1000 kHz, i.e., more than three orders of magnitude slower than hardware. Improved simulators can increase designers' productivity by speeding design iterations and permitting more exhaustive exploration. Mahyar Emami, Sahand Kashani, Keisuke Kamahori, Mohammad Sepehr Pourghannad, Ritik Raj, James R. Larus |
ASPLOS (4) | 4 |