Translated by Google
Course on Digital Circuits: FPGA, VHDL, SYSTEMVERILOG, UVM.
From 15.49 £ /h
If you're seeking to gain a competitive edge in the VLSI field, you have found the ideal resource. As a seasoned Hardware Design Verification Engineer, I offer my tutoring services to individuals interested in delving into Digital Electronics, as well as FPGA/ASIC/SoC Circuits. Whether you require assistance in Design (VHDL, VERILOG, SYSTEMVERILOG) or Verification (Simulation Synopsys), I am well-equipped to provide the support you need. Please feel free to reach out to me without hesitation.
Location
Online from Morocco
About Me
I am a hardware design verification engineer and a PhD student in deep learning and embedded systems, if you are looking for courses on:
digital electronics, design and verification processes, artificial intelligence, machine learning and deep learning, then you are in the right place.
Skills:
• Artificial intelligence, machine learning and deep learning
• Verification methodologies: UVM, C-driven, formal (SVA), OOP, constrained random verification, metric-driven verification, UVM and C based testbenches, code coverage.
• Languages: System Verilog, C, C++, Tcl, Verilog, VHDL, Makefile, Python, XML.
• Simulation: VCS/Synopsys.
• Data management, tracking: SVN, JIRA.
• Flow: verification plan, schedules, VIP, test benches, sequences and test cases, integration tests, coverage, checkers and assertions, regressions.
digital electronics, design and verification processes, artificial intelligence, machine learning and deep learning, then you are in the right place.
Skills:
• Artificial intelligence, machine learning and deep learning
• Verification methodologies: UVM, C-driven, formal (SVA), OOP, constrained random verification, metric-driven verification, UVM and C based testbenches, code coverage.
• Languages: System Verilog, C, C++, Tcl, Verilog, VHDL, Makefile, Python, XML.
• Simulation: VCS/Synopsys.
• Data management, tracking: SVN, JIRA.
• Flow: verification plan, schedules, VIP, test benches, sequences and test cases, integration tests, coverage, checkers and assertions, regressions.
Education
Doctoral student: Development/optimization of embedded systems and image processing algorithms for the detection of traffic offenses in real time.
Engineer in embedded electronic systems.
Engineer in embedded electronic systems.
Experience / Qualifications
2+ years as a verification engineer.
1 year+ as an automotive software engineer.
Skills:
• Artificial intelligence, machine learning and deep learning
• Verification methodologies: UVM, C-driven, formal (SVA), OOP, constrained random verification, metric-driven verification, UVM and C based testbenches, code coverage.
• Languages: System Verilog, C, C++, Tcl, Verilog, VHDL, Makefile, Python, XML.
• Simulation: VCS/Synopsys.
• Data management, tracking: SVN, JIRA.
• Flow: verification plan, schedules, VIP, test benches, sequences and test cases, integration tests, coverage, checkers and assertions, regressions.
1 year+ as an automotive software engineer.
Skills:
• Artificial intelligence, machine learning and deep learning
• Verification methodologies: UVM, C-driven, formal (SVA), OOP, constrained random verification, metric-driven verification, UVM and C based testbenches, code coverage.
• Languages: System Verilog, C, C++, Tcl, Verilog, VHDL, Makefile, Python, XML.
• Simulation: VCS/Synopsys.
• Data management, tracking: SVN, JIRA.
• Flow: verification plan, schedules, VIP, test benches, sequences and test cases, integration tests, coverage, checkers and assertions, regressions.
Age
Preschool children (4-6 years old)
Children (7-12 years old)
Teenagers (13-17 years old)
Adults (18-64 years old)
Seniors (65+ years old)
Student level
Beginner
Intermediate
Duration
60 minutes
The class is taught in
English
French
Arabic
Skills
Availability of a typical week
(GMT -05:00)
New York
Mon
Tue
Wed
Thu
Fri
Sat
Sun
00-04
04-08
08-12
12-16
16-20
20-24
The aim of this program is to provide you with the necessary skills and experience to begin your journey and get a head-start in Machine Learning.
Covering the primary types of machine learning, the program offers a comprehensive theoretical understanding of Machine Learning with opportunities to practice using algorithms, methods,
and best practices associated with Machine Learning. You will also have the chance to develop your own projects using relevant open-source frameworks and
libraries and apply your learnings in various courses to a final project.
Whether you are already proficient in Python programming, statistics, and linear algebra, or have a general interest and are willing to learn,
this beginner/intermediate oriented series is suitable for you.
Covering the primary types of machine learning, the program offers a comprehensive theoretical understanding of Machine Learning with opportunities to practice using algorithms, methods,
and best practices associated with Machine Learning. You will also have the chance to develop your own projects using relevant open-source frameworks and
libraries and apply your learnings in various courses to a final project.
Whether you are already proficient in Python programming, statistics, and linear algebra, or have a general interest and are willing to learn,
this beginner/intermediate oriented series is suitable for you.
Good-fit Instructor Guarantee