Forwarded from Embedded Doka (Dmitry Murzinov)
sv2chisel - SystemVerilog to Chisel Translator
💾 Code
📄 Paper
📄 Features & Limitations
#SV #verilog #chisel #translator
@embedoka
💾 Code
📄 Paper
📄 Features & Limitations
#SV #verilog #chisel #translator
@embedoka
Rosetta - Realistic High-level Synthesis Benchmark Suite for Software Programmable FPGAs
It contains six fully-developed applications from machine learning and image/video processing domains, where each benchmark consists multiple compute kernels that expose diverse sources of parallelism.
These applications are developed under realistic design constraints, and are optimized at both kernel-level and application-level with the advanced features of HLS tools to meet these constraints.
💾 Code
📄 Paper
#hls #fpga #benchmark
@fpgasic
It contains six fully-developed applications from machine learning and image/video processing domains, where each benchmark consists multiple compute kernels that expose diverse sources of parallelism.
These applications are developed under realistic design constraints, and are optimized at both kernel-level and application-level with the advanced features of HLS tools to meet these constraints.
💾 Code
📄 Paper
#hls #fpga #benchmark
@fpgasic
VCD command line viewer for Windows, Linux and MacOS
Features:
▫️Outputs in YAML format
▫️Querying (unix pipe)
▫️Styling
▫️Auto-Refresh
💾 https://github.com/yne/vcd
#vcd #dump #waveform #testbench #waves #documentation
@fpgasic
Features:
▫️Outputs in YAML format
▫️Querying (unix pipe)
▫️Styling
▫️Auto-Refresh
💾 https://github.com/yne/vcd
#vcd #dump #waveform #testbench #waves #documentation
@fpgasic
VeriGen: A Large Language Model for Verilog Code Generation
The next step getting a Verilog fine-tuned LLM to generate the RTL; then we can kick-back & watch the whole SoC emerge autonomously. Upon testing with a more diverse and complex problem set, we find that the fine-tuned model shows competitive performance against state-of-the-art gpt-3.5-turbo, excelling in certain scenarios. Notably, it demonstrates a 41% improvement in generating syntactically correct Verilog code across various problem categories compared to its pre-trained counterpart, highlighting the potential of smaller, in-house LLMs in hardware design automation.
💾 https://arxiv.org/abs/2308.00708
#verilog #sv #codegen #llm
@fpgasic
The next step getting a Verilog fine-tuned LLM to generate the RTL; then we can kick-back & watch the whole SoC emerge autonomously. Upon testing with a more diverse and complex problem set, we find that the fine-tuned model shows competitive performance against state-of-the-art gpt-3.5-turbo, excelling in certain scenarios. Notably, it demonstrates a 41% improvement in generating syntactically correct Verilog code across various problem categories compared to its pre-trained counterpart, highlighting the potential of smaller, in-house LLMs in hardware design automation.
💾 https://arxiv.org/abs/2308.00708
#verilog #sv #codegen #llm
@fpgasic
Forwarded from VLSI HUB (Dmitry Murzinov)
Open-source IC cells as 3D prints. A rough how-to guide
This util can interpret so called IC layouts and render them in 3D. The program accepts standard GDSII files as input data. Along with the layout file, it requires a so called process definition file which contains the 3D parameters of the process being used.
📄 https://medium.com/@thorstenknoll/open-source-ic-cells-as-3d-prints-a-rough-how-to-guide-90a8bc8b3b57
💾 https://github.com/trilomix/GDS3D
@vlsihub
This util can interpret so called IC layouts and render them in 3D. The program accepts standard GDSII files as input data. Along with the layout file, it requires a so called process definition file which contains the 3D parameters of the process being used.
📄 https://medium.com/@thorstenknoll/open-source-ic-cells-as-3d-prints-a-rough-how-to-guide-90a8bc8b3b57
💾 https://github.com/trilomix/GDS3D
@vlsihub
OpenVAF - an innovative Verilog-A compiler for use in circuit simulator. The major aim of this Project is to provide a high-quality standard compliant compiler for Verilog-A.
Features:
▫️fast compile times (usually below 1 second for most compact models)
▫️high-quality user interface
▫️easy setup
▫️fast simulations surpassing existing solutions by 30%-60%
OpenVAF currently contains the following useable projects:
1️⃣ VerilogAE allows obtaining model equations (calculates the value of a single Variable) from Verilog-A files
2️⃣ Melange is an experimental circuit simulator that leverage OpenVAF to gain access to compact models
Links:
📄 openvaf.semimod.de/
💾 github.com/pascalkuthe/OpenVAF
#simulation #model #veriloga
@fpgasic
Features:
▫️fast compile times (usually below 1 second for most compact models)
▫️high-quality user interface
▫️easy setup
▫️fast simulations surpassing existing solutions by 30%-60%
OpenVAF currently contains the following useable projects:
1️⃣ VerilogAE allows obtaining model equations (calculates the value of a single Variable) from Verilog-A files
2️⃣ Melange is an experimental circuit simulator that leverage OpenVAF to gain access to compact models
Links:
📄 openvaf.semimod.de/
💾 github.com/pascalkuthe/OpenVAF
#simulation #model #veriloga
@fpgasic
Forwarded from VLSI HUB (Dmitry Murzinov)
Generate Compilers from Hardware Models
Compiler backends should be automatically generated from hardware design language (HDL) models of the hardware they target. Generating compiler components directly from HDL can provide stronger correctness guarantees, ease development effort, and encourage hardware exploration. Past work has already championed this idea; here we argue that advances in program synthesis make the approach more feasible. We present a concrete example by demonstrating how FPGA technology mappers can be automatically generated from SystemVerilog models of an FPGA's primitives using program synthesis.
💾 https://arxiv.org/abs/2305.09580
@vlsihub
Compiler backends should be automatically generated from hardware design language (HDL) models of the hardware they target. Generating compiler components directly from HDL can provide stronger correctness guarantees, ease development effort, and encourage hardware exploration. Past work has already championed this idea; here we argue that advances in program synthesis make the approach more feasible. We present a concrete example by demonstrating how FPGA technology mappers can be automatically generated from SystemVerilog models of an FPGA's primitives using program synthesis.
💾 https://arxiv.org/abs/2305.09580
@vlsihub
Ramulator - a Fast and Extensible DRAM Simulator, with built-in support for modeling many different DRAM technologies, and various academic proposals.
Ramulator supports a wide array of commercial DRAM standards:
▫️DDR3, DDR4
▫️LPDDR3, LPDDR4
▫️GDDR5
▫️WIO, WIO2
▫️HBM
▫️SALP
▫️TL-DRAM
▫️RowClone
▫️DSARP
Links:
📄 http://users.ece.cmu.edu/~omutlu/pub/ramulator_dram_simulator-ieee-cal15.pdf
💾 https://github.com/CMU-SAFARI/ramulator
#ram #model #dram #ddr #hbm
@fpgasic
Ramulator supports a wide array of commercial DRAM standards:
▫️DDR3, DDR4
▫️LPDDR3, LPDDR4
▫️GDDR5
▫️WIO, WIO2
▫️HBM
▫️SALP
▫️TL-DRAM
▫️RowClone
▫️DSARP
Links:
📄 http://users.ece.cmu.edu/~omutlu/pub/ramulator_dram_simulator-ieee-cal15.pdf
💾 https://github.com/CMU-SAFARI/ramulator
#ram #model #dram #ddr #hbm
@fpgasic
Ramulator v2 - a modern, modular, extensible, and fast cycle-accurate DRAM simulator. It provides support for agile implementation and evaluation of new memory system designs (e.g., new DRAM standards, emerging RowHammer mitigation techniques).
Ramulator 2.0 provides the DRAM models for the following standards:
▫️DDR3, DDR4, DDR5
▫️LPDDR5
▫️GDDR6
▫️HBM2, HBM3
Links:
📄 https://people.inf.ethz.ch/omutlu/pub/Ramulator2_arxiv23.pdf
💾 https://github.com/CMU-SAFARI/ramulator2
#ram #model #dram #ddr #hbm
@fpgasic
Ramulator 2.0 provides the DRAM models for the following standards:
▫️DDR3, DDR4, DDR5
▫️LPDDR5
▫️GDDR6
▫️HBM2, HBM3
Links:
📄 https://people.inf.ethz.ch/omutlu/pub/Ramulator2_arxiv23.pdf
💾 https://github.com/CMU-SAFARI/ramulator2
#ram #model #dram #ddr #hbm
@fpgasic
CompressedLUT - a tool for lossless compression of lookup tables and generation of their hardware files in Verilog and C++ for RTL and HLS designs.
Links:
📄 https://doi.org/10.1145/3626202.3637575
💾 https://github.com/kiabuzz/CompressedLUT
#acceleration #LUT #lookuptable #lossless #compression #table-size-reduction #table-based-function-implementation
@fpgasic
Links:
📄 https://doi.org/10.1145/3626202.3637575
💾 https://github.com/kiabuzz/CompressedLUT
#acceleration #LUT #lookuptable #lossless #compression #table-size-reduction #table-based-function-implementation
@fpgasic
Forwarded from VLSI HUB (Dmitry Murzinov)
DUTCTL: A Flexible Open-Source Framework for Rapid Bring-Up, Characterization, and Remote Operation of Custom-Silicon RISC-V SoCs
https://pulp-platform.org/docs/riscvmunich2024/RISCV_europe_summit_2024_DUTCTL_poster.pdf
@vlsihub
https://pulp-platform.org/docs/riscvmunich2024/RISCV_europe_summit_2024_DUTCTL_poster.pdf
@vlsihub
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FloorSet: The First Million-Scale Dataset for SoC Design Planning.
FloorSet is aimed at advancing machine learning for floorplanning physical layouts of systems-on-a-chip (SoCs) and its sub-systems. It features 2 million synthetic benchmark circuits that capture real design constraints and objectives, all carefully sampled from actual design distributions.
FloorSet addresses key challenges of training-data availability and reproducibility in the chip-design world, driving fundamental research in large-scale constrained optimization problems.
Links:
📄 https://arxiv.org/abs/2405.05480
💾 https://github.com/IntelLabs/FloorSet
#floorplan #SoC #MLaimed #CAE #opensource
@fpgasic
FloorSet is aimed at advancing machine learning for floorplanning physical layouts of systems-on-a-chip (SoCs) and its sub-systems. It features 2 million synthetic benchmark circuits that capture real design constraints and objectives, all carefully sampled from actual design distributions.
FloorSet addresses key challenges of training-data availability and reproducibility in the chip-design world, driving fundamental research in large-scale constrained optimization problems.
Links:
📄 https://arxiv.org/abs/2405.05480
💾 https://github.com/IntelLabs/FloorSet
#floorplan #SoC #MLaimed #CAE #opensource
@fpgasic
Shuhai is a benchmarking-memory tool that allows FPGA programmers to demystify all the underlying details of memories, e.g., HBM and DDR4, on a Xilinx FPGA
In terms of benchmarking memory, it can be done better on FPGA, rather than on CPU/GPU. When benchmarking memory on an FPGA, the benchmarking hardware engine can directly attach to the memory such that there is no noise between the targeted memory and the benchmarking engine.
With Shuhai, before implementing the concrete application that contains a particular memory access pattern on the FPGA, we are able to benchmark the corresponding memory access pattern to make sure that the memory side will not be the bottleneck.
Links:
📄 https://ieeexplore.ieee.org/document/9114755
💾 https://github.com/RC4ML/Shuhai
#memory #ddr #hbm #benchmark
@fpgasic
In terms of benchmarking memory, it can be done better on FPGA, rather than on CPU/GPU. When benchmarking memory on an FPGA, the benchmarking hardware engine can directly attach to the memory such that there is no noise between the targeted memory and the benchmarking engine.
With Shuhai, before implementing the concrete application that contains a particular memory access pattern on the FPGA, we are able to benchmark the corresponding memory access pattern to make sure that the memory side will not be the bottleneck.
Links:
📄 https://ieeexplore.ieee.org/document/9114755
💾 https://github.com/RC4ML/Shuhai
#memory #ddr #hbm #benchmark
@fpgasic