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Sahand Kashani

dblp:338/6764 · DBLP profile ↗
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4ranked-venue papers
2as first author
4since 2021 · last 2024
0000-0002-6198-4969ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 3 · 2 first-author · 3 since 2021Software engineering, systems software and programming languages · 2 · 2 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 · 28% Hardware accelerators and domain-specific architectures · 23% Reconfigurable computing and FPGAs · 16%

Topics — the 8 heaviest of 11, 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
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
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

performance interfaces · 0.8static 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
FPGA1
2024 Performance Interfaces for Hardware Accelerators
Jiacheng Ma 0002, Rishabh Iyer 0002, Sahand Kashani, Mahyar Emami, Thomas Bourgeat, George Candea
OSDI3
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)2
2022 Bitfiltrator: A general approach for reverse-engineering Xilinx bitstream formats
abstract
As the usage of FPGAs spreads, engineers will inevitably employ them in ways unforeseen-or unwanted-by their manufacturers. Xilinx's toolchains offer multiple points for customizing the FPGA compilation flow, but all flows must end with Vivado as it is the only tool capable of generating the bitstream to program an FPGA. Xilinx does not document its bitstream format, so users who wish to bypass Vivado and modify a bitstream directly must reverse-engineer it to discover the location and format of cells. Prior work has reverse-engineered parts of the bitstream format for security or debugging/instrumentation activities, but no paper has explained how to do this reverse engineering systematically! Code from prior efforts (when available) is hard-coded to reverse engineer a specific device and is difficult or impossible to use for another one. These efforts-focused on applications instead of reverse-engineering-compel engineers who need to modify a bitstream to rediscover unwritten practice. Our work bridges this gap by explaining: (1) the various parameters needed to navigate a bitstream correctly, (2) the experiments to obtain them, and (3) the many pitfalls and erroneous assumptions to avoid while undertaking this endeavor. We demonstrate our technique by using it to extract the bitstream format of initial LUT equations, LUTRAM contents, BRAM contents, and register values in Xilinx UltraScale and UltraScale+ FPGAs. Our methods are implemented in an open-source tool, Bitfiltrator [1], that can extract device layouts and architecture-specific bitstream formats for these cells automatically and without physical access to an FPGA.
Sahand Kashani, Mahyar Emami, James R. Larus
FPL1