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Mohammad Sepehr Pourghannad

dblp:338/6343 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Electronic design automation › hardware verification and test
hardware verification
0.922024
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
0.922024
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
0.812024
Pecan: Cost-Efficient ML Data Preprocessing with Automatic Transformation Ordering and Hybrid Placement · USENIX ATC 2024
Data integration and cleaning
data preprocessing
0.812024
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
0.712023
Manticore: Hardware-Accelerated RTL Simulation with Static Bulk-Synchronous Parallelism · ASPLOS (4) 2023
Hardware accelerators and domain-specific architectures › scientific computing accelerator
simulation accelerator
0.712023
Manticore: Hardware-Accelerated RTL Simulation with Static Bulk-Synchronous Parallelism · ASPLOS (4) 2023
Cloud and datacenter computing
cluster resource management and scheduling
0.212024
Pecan: Cost-Efficient ML Data Preprocessing with Automatic Transformation Ordering and Hybrid Placement · USENIX ATC 2024
Processor architecture and microarchitecture
many-core architecture
0.212024
A 475 MHz Manycore FPGA Accelerator for RTL Simulation · FPGA 2024
Reconfigurable computing and FPGAs › FPGA-based processor implementation
soft-core processor
0.212024
A 475 MHz Manycore FPGA Accelerator for RTL Simulation · FPGA 2024
Performance modeling and evaluation › simulation › architectural simulation
cycle-accurate simulation
0.212023
Manticore: Hardware-Accelerated RTL Simulation with Static Bulk-Synchronous Parallelism · ASPLOS (4) 2023
Integrated circuit design › digital system design
register-transfer level design
0.212023
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
YearPublicationVenuePosition
2024 A 475 MHz Manycore FPGA Accelerator for RTL Simulation
abstract
This 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
FPGA4
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 ATC4
2023 Manticore: Hardware-Accelerated RTL Simulation with Static Bulk-Synchronous Parallelism
abstract
The 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