VLSI & AI Chip Design · Practical career preparation

AI Chip Design RISC-V & AI Accelerator Engineering

Build an AI accelerator—and measure what it improves. Design a small quantized compute engine, integrate it with a supplied RISC-V platform and compare its results against an independent software reference. Explore the arithmetic, memory movement and firmware decisions behind useful acceleration.

11 core modules Preparation matched to your diagnostic Three guided projects integrated into the curriculum One independently assessed capstone

RoboEdify track record

10,000+

alumni transformed

1,000+

hiring partners

4.8/5

average class rating

87%

placed in 6 months

10+

years of training

Where our AI alumni work

Microsoft

Amazon

Salesforce

ServiceNow

Deloitte

Infosys

Accenture

TCS

Wipro

Capgemini

Cognizant

HCL

Microsoft

Amazon

Salesforce

ServiceNow

Deloitte

Infosys

Accenture

TCS

Wipro

Capgemini

Cognizant

HCL

Direct answer

What will you design in an AI chip-design course?

You will design accelerator hardware that executes a bounded AI computation, such as an integer matrix-vector operation. A supplied RISC-V system controls the accelerator, while your work focuses on its datapath, buffers, interfaces and measured behavior. This is an advanced RTL and hardware/software integration pathway.

The learning journey

01Choose a workload.

Define shapes, numeric behavior and a software baseline.

02Design the engine.

Build arithmetic, pipelines and local buffering.

03Integrate.

Connect control registers, data movement and firmware.

04Evaluate.

Check correctness and compare complete-system performance.

Why this course focuses on practical evidence

More compute only helps when the rest of the system can feed it.

You will measure transfer overhead, inspect integer arithmetic and compare dataflow choices. The final report explains where acceleration helps, where it does not and what the measurements actually include.

Learning format

Learn through classes, labs and individual feedback.

The program combines live mentor-led classes, guided labs, project work and support sessions. Your diagnostic determines the preparation needed before the common core.

Choose on-campus, live online or a working-professional format. An advisor can explain the class format, lab access and learner support before enrollment.

On-campusLive onlineWorking-professional format

Who should join

A common core, with preparation matched to your starting point.

Graduates and learners entering the fieldJoin after RTL Design & FPGA Prototyping or equivalent demonstrated competence. Preparation bridges architecture, C/Python and quantized inference; it does not teach RTL from zero.

Working engineersRTL, FPGA and embedded engineers can use the diagnostic to identify gaps in numeric modeling, architecture or integration. Every learner completes the same hardware-correctness and benchmark assessment.

Learning outcomes

Leave with skills you can demonstrate.

Define a measurable workloadState tensor shapes, outputs and comparison conditions.

Build a bit-exact referenceExplain integer scaling, rounding, clipping and accumulation.

Design accelerator RTLImplement MAC pipelines and control behavior.

Plan data movementBudget buffers and compare reuse and bandwidth.

Integrate hardware and firmwareControl the engine from a supplied RISC-V platform.

Defend performance claimsInclude transfer overhead, resource reports and numeric correctness.

Preparation based on your diagnostic

What you practise before the core.

C/Python and reference modelsImplement deterministic numeric references, binary formats and a small bare-metal program.

Computer architecture and RISC-V readinessTrace instructions, memory maps, interrupts and software/hardware register access.

Linear algebra and quantized inferenceDot products, tensor shapes, scales/zero points, rounding and clipping.

RTL/FPGA readiness integrationIntegrate a supplied peripheral and independently check arithmetic; remediate narrowly scoped gaps.

Both routes complete the same practical exit requirements. A diagnostic identifies the preparation you need. Previously demonstrated foundations can be recognized, while broader gaps receive a separate learning plan before the core.

Course curriculum

11 modules. A complete path from foundations to an independent capstone.

Each module combines technical concepts, practical work, a failure investigation and a reviewed submission. Preparation sits before the common core.

Workload definition and acceptance

AI Chip Design

Choose a computation with clear comparison boundaries.

Topics

•Kernel selection, tensor shapes, accuracy budget, operation count, memory traffic and end-to-end boundaries

Hands-on lab

Profile a small inference workload and choose one matrix-vector/dot-product kernel.

Failure scenario you solve

The selected kernel is fast, but it contributes little to the application’s total runtime.

You build

Workload contract and baseline measurement plan.

Assessed outcome: Explain why kernel speed alone may not improve the application.

Quantization and numeric correctness

AI Chip Design

Make every integer result explainable.

Topics

•Signed integer arithmetic, scale/zero point, accumulation width, bias, requantization, rounding and saturation

Hands-on lab

Create bit-exact Python references and test extreme positive/negative cases against floating-point outputs.

Failure scenario you solve

A model matches on average inputs but fails near saturation because rounding rules differ.

You build

Numeric specification and golden vectors.

Assessed outcome: Explain every rounding and overflow rule and reproduce expected integers.

RISC-V execution and platform integration

AI Chip Design

Bring up the supplied platform before adding complexity.

Topics

•Pinned ISA subset, supplied core, toolchain, reset vector, memory map, interrupts and bare-metal build

Hands-on lab

Run an unmodified supplied core, inspect disassembly and add a memory-mapped test peripheral.

Failure scenario you solve

Firmware writes to the wrong memory map and never reaches the new peripheral.

You build

Platform manifest and firmware bring-up log.

Assessed outcome: Distinguish ISA requirements from implementation-specific platform choices.

MAC datapath and pipeline design

AI Chip Design

Build a correctly sized and aligned compute pipeline.

Topics

•Multiplier/accumulator sizing, pipeline stages, enables, throughput, result validity and reset flushing

Hands-on lab

Implement a parameterized integer MAC/dot-product datapath and compare with the golden model.

Failure scenario you solve

An accumulator overflows or a valid flag refers to the wrong result.

You build

RTL and corner-case regression.

Assessed outcome: Detect overflow and pipeline alignment defects under stalls.

Dataflow, buffers and bandwidth

AI Chip Design

Design around the data the engine needs.

Topics

•Weight/output-stationary concepts, tiling, local storage, reuse, bank conflicts and bandwidth ceilings

Hands-on lab

Compare two bounded dataflow/buffering choices on the same workload.

Failure scenario you solve

Additional MAC units remain idle because the buffer cannot supply operands fast enough.

You build

Traffic model, buffer budget and trace-backed comparison.

Assessed outcome: Identify a memory bottleneck rather than assuming more MACs always help.

Accelerator control and bus interface

AI Chip Design

Define control and ownership across the interface.

Topics

•Register map, launch/completion, busy/error states, data transport, ordering and interrupts

Hands-on lab

Integrate a simple memory-mapped accelerator with a documented transaction and buffer-ownership contract.

Failure scenario you solve

A repeated start command overwrites an active operation.

You build

Interface spec, RTL wrapper and integration tests.

Assessed outcome: Handle repeated start, reset while busy and invalid configuration deterministically.

Firmware and software reference path

AI Chip Design

Measure the software path and accelerated path fairly.

Topics

•Driver-style APIs, polling versus interrupts, buffer preparation, result extraction and workload reproducibility

Hands-on lab

Build a bare-metal software baseline and accelerated path using identical inputs and output rules.

Failure scenario you solve

A benchmark excludes input transfer from only the accelerated result.

You build

Firmware API and end-to-end test harness.

Assessed outcome: Include transfer/setup overhead in application timing.

Verification and stress testing

AI Chip Design

Test arithmetic and integration independently.

Topics

•Independent scoreboards, random dimensions within scope, boundary values, assertions, error injection and reset

Hands-on lab

Build layered arithmetic/interface/system tests and reduce injected failures.

Failure scenario you solve

Golden arithmetic tests pass while reset leaves stale completion status in the system.

You build

Regression suite and defect reports.

Assessed outcome: Demonstrate detection of both numeric and integration defects.

FPGA implementation and performance

AI Chip Design

Separate measured results from estimates.

Topics

•Device constraints, resource mapping, timing closure, cycle counters, throughput/latency and measurement variability

Hands-on lab

Deploy on the qualified board and compare software versus accelerator paths under fixed conditions.

Failure scenario you solve

Simulated cycle counts are reported as measured FPGA performance.

You build

Device reports, raw measurements and benchmark script.

Assessed outcome: Separate simulated cycles, synthesis estimates and measured hardware performance.

Independent accelerator capstone

Capstone

Compare two designs under the same conditions.

Topics

•Freeze numeric and platform scope, implement two architecture variants, integrate firmware and evaluate

Hands-on lab

Deliver a small int8 matrix-vector engine controlled by the supplied RISC-V core.

Failure scenario you solve

A claimed improvement comes from changing the clock or numeric workload between runs.

You build

Reproducible hardware/software package and comparison report.

Assessed outcome: Meet correctness and platform budgets, then defend measured tradeoffs.

Interview and architecture defense

Career preparation

Defend architecture choices with evidence.

Topics

•Data movement, arithmetic, RTL review and benchmark interpretation

Hands-on lab

Explain one bottleneck and debug an unseen numeric or interface defect.

Failure scenario you solve

An assessor asks whether a larger buffer would help more than another multiplier.

You build

Portfolio case study and individual defense.

Assessed outcome: Support claims with traces, equations and measured results.

Scope note: Core depth is accelerator hardware and HW/SW integration. No full custom CPU, large-model accelerator, ASIC tapeout, advanced compiler backend or production NPU is promised. HLS can be a comparison extension; the core assessed datapath is RTL. AI-assisted EDA is separate from designing hardware that executes AI.

Tools and methods

Use a focused stack to build and explain your work.

SystemVerilog and a qualified simulatorImplement and test accelerator hardware.

Python reference modelsDefine and verify numeric behavior.

Supplied RISC-V core and toolchainRun the control firmware and software baseline.

FPGA implementation and debug toolsMeasure the design on the selected device.

C, Git and benchmark scriptsBuild reproducible hardware/software comparisons.

The course uses a supplied RISC-V platform and a qualified FPGA board/toolchain combination. The supported ISA subset, numeric limits and hardware access arrangements will be specified. Energy results are reported only when instrument measurements are available.

Projects and portfolio

Three guided projects, followed by an independent capstone.

The guided projects develop across the modules and are part of the core curriculum.

Guided project 1

Quantized numeric reference

Create bit-exact arithmetic tests and compare the integer computation with its floating reference.

Portfolio evidence

Numeric contractGolden vectorsError analysis

Guided project 2

Parameterized MAC engine

Implement and independently verify a pipelined integer datapath under boundary values and stalls.

Portfolio evidence

RTLScoreboardStress tests

Guided project 3

RISC-V-controlled accelerator peripheral

Connect a simple compute block to firmware through documented registers and a completion mechanism.

Portfolio evidence

Capstone

Int8 matrix-vector accelerator on a RISC-V/FPGA platform

Implement and compare two bounded compute or buffering variants on a supplied RISC-V FPGA system. Use the same workload and numeric contract, demonstrate correctness and explain end-to-end performance and resource tradeoffs.

What you submit

•A clear scope, design or integration plan, and acceptance checklist.
•Your source files, scripts and configuration with a readable project guide.
•Numeric references, accelerator RTL, firmware integration and matched benchmark evidence.
•A failure investigation showing the cause, correction and recheck.
•A final report explaining results, assumptions and remaining limitations.
•An individual walkthrough and an unfamiliar debugging task.

What the assessor checks: Your implementation addresses the agreed project requirements, tests the relevant boundary and failure cases, and can be reproduced from the submitted materials. You explain your own contribution and support conclusions with actual results. A polished group demonstration alone does not meet the individual exit requirement.

Assessment and completion

Demonstrate what you can do.

RoboEdify · Certificate of Completion

AI Chip Design: RISC-V & AI Accelerator Engineering

Presented to

Learner name

Awarded for completing the course's practical assessments and independently defending its capstone project.

Manikanta Kona

Founder · RoboEdify

ROBO
EDIFY
CERT

30%

Module labs

20%

Practical checkpoints

35%

Capstone

15%

Individual debugging and defense

The proposed completion standard is 70% overall, at least 60% separately in the capstone and individual defense, and completion of all mandatory practical requirements. Feedback identifies specific gaps for remediation and reassessment.

This is a proposed RoboEdify course credential. External accreditation, vendor certification and partner endorsement are not implied by the course title.

Career preparation

Prepare for relevant roles with work you can explain.

Accelerator RTL/Prototyping TraineePractise bounded compute-engine design and validation.

Junior Hardware/Software Integration EngineerDevelop firmware, register-interface and system-debugging skills.

FPGA AI-Acceleration TraineeBuild experience with numeric correctness and workload-based evaluation.

Your career-preparation work includes a reviewed technical project summary, a readable repository, resume statements grounded in your contribution and a live technical interview. Role eligibility depends on each employer's requirements and your demonstrated skills.

Career support

Build your portfolio. Prepare your profile. Practise your interviews.

Career support includes portfolio and profile preparation, interview practice, and role-fit introductions where available. The shared hiring-partner network includes Infosys, TCS, Deloitte, Accenture, Cognizant, NTT Data and Capgemini.

01 / PORTFOLIO

Evidence from your own work.

For this course, your portfolio centres on numeric references, accelerator RTL, firmware integration and matched benchmark evidence you complete.

02 / PROFILE

Profile and resume preparation.

A reviewed technical project summary, a readable repository and resume statements grounded in your contribution.

03 / INTERVIEWS

Interview practice and introductions.

Technical interview practice, with role-fit introductions where available.

RoboEdify does not guarantee an interview, offer, salary, employer, location or timeline.

Institute leadership

Meet the team behind RoboEdify.

Manikanta Kona

Founder, RoboEdify · Enterprise AI Architect

Enterprise AI · Agentic Systems · LLM Platforms · Robotics & Edge AI

15 yrs

ENTERPRISE AI

2,400+

LEARNERS

4.9 /5

RATING

Manikanta brings 15 years of enterprise platform architecture experience from AT&T, Salesforce, Cox Communications and Broadcom. His background includes enterprise platform and AI rollouts for Fortune-500 banks, telcos and insurers, and production agentic-AI deployments for governed case handling.

Education: M.S. in Engineering, Purdue University.

Ravi Krishna

Chief Technologist, RoboEdify · Implementation & Delivery Lead

Enterprise automation · Deployment · Evaluation evidence · Delivery governance

10 yrs

IMPLEMENTATION & DELIVERY

1,800+

LEARNERS

4.8 /5

RATING

Ravi leads RoboEdify's implementation and delivery practice. His background spans enterprise automation programs, deployment, evaluation evidence and delivery governance.

Industry voices from RoboEdify’s AI programs

What employers say about RoboEdify’s AI and robotics graduates.

The following testimonials retain their original program context. They describe AI, robotics and enterprise-program experience, rather than outcomes from this course.

RoboEdify grads ramp 40% faster on applied AI projects than typical hires. Best AI engineering pipeline in India.

Aakash Mehta, Partner Programme Lead, Microsoft

We've onboarded 80+ RoboEdify alumni in 18 months. Lowest ramp time we've seen for ML plus AI agent practices.

Anita Sharma, Senior Manager, Deloitte

The programme is comprehensive — predictive ML, LLM systems, plus agentic and robotics work. Grads come pre-trained for enterprise.

Rahul Bhatt, Solutions Lead, Mphasis

Their agent + robotics track produces engineers who ship production-grade perception and control code on day one. Genuinely rare.

Deepak Pillai, Senior Architect, TCS

What sets RoboEdify apart is the simulation-to-hardware layer baked into the AI track. Our clients ask for exactly this profile.

Suresh Menon, Practice Lead, Accenture

Their fundamentals prep is rigorous, and the capstone with a real deployed system and safety case is what closes interviews for us.

Vikram Iyer, Director, Infosys

RoboEdify's AI grads get models into production twice as fast in the first 90 days. Our internal metrics back this up clearly.

Lakshmi Nair, VP Engineering, Wipro

Best AI + robotics pipeline we've sourced from in India. Their projects are production work, not toy code.

Karthik Subramanian, Engineering Director, Cognizant

Strong ML and edge-deployment foundation. Their grads need almost zero ramp time on enterprise engagements with us.

Arun Joshi, Practice Director, Capgemini

We've placed 40+ RoboEdify alumni across our AI and automation teams. Strong fundamentals, sharp on the agent stack.

Sanjay Verma, Talent Director, IBM

ITOM + Predictive Intelligence is exactly the talent gap we've been struggling to close. RoboEdify is filling it for us reliably.

Anjali Desai, Practice Head, LTIMindtree

Their AI track delivers engineers who navigate data, models and integrations on customer engagements unsupervised.

Ramesh Iyer, Senior Manager, Tech Mahindra

Hired 25+ RoboEdify graduates for our AI practice. Strong coding, strong ML depth, sharp on the agent layer.

Geetha Pillai, Talent Acquisition Lead, Cyient

RoboEdify grads who blend robotics with Azure OpenAI land production-ready on day one. Rare combination, well-trained.

Priya Reddy, Talent Lead, Microsoft

AI alumni across RoboEdify

Meet alumni featured in our AI programs.

Spandana Bala

ML Engineer

Hyderabad · India

Now at · Infosys

Naveen Vedala

AI Agent Engineer

Hyderabad · India

Now at · TCS

Tejashwini Addla

Simulation Engineer

Hyderabad · India

Now at · Deloitte

Tharunesh Dillikar

Robotics Software Engineer

Seattle · United States

Now at · Accenture

Mujahed Mohammed

Edge AI Engineer

Hyderabad · India

Now at · Accenture

Bhargav Kumar Murala

Physical AI Engineer

Hyderabad · India

Now at · Capgemini

Sai Manasa Leburi

Autonomous Systems Engineer

New York · United States

Now at · NTT Data

Rahul Dhamma

Robot Learning Engineer

Hyderabad · India

Now at · Cognizant

Our locations

Come chat with us—on campus or online.

Flagship campus

Hyderabad

2nd Floor, Hitech City Road · Above Domino's · Opp. Cyber Towers, Jai Hind Enclave · Hyderabad, Telangana

Call

+91 8142998866

US desk

+1 256 388 7766

Opening hours

Mon–Sun · 7 AM–9 PM

Online

Global

Live online classes and mentorship, with working-professional learning options. AI hardware labs use a qualified RISC-V/FPGA platform, with access arrangements explained before enrollment.

Format

Live online + mentorship

Options

Working-professional

Learning support

Support when you need to catch up.

Freeze your seat for up to 90 days and rejoin the next class at no extra fee. TAs run catch-up sessions every Saturday, and recordings of every live session are available for the lifetime of your account.

FAQs

Questions about prerequisites, tools and completion.

Can I start without RTL experience?+

This is an advanced course. You first need synthesizable RTL, independent-testbench and FPGA/synthesis readiness, gained through the RTL course or equivalent experience.

Will I design an entire chip or CPU?+

The core designs an accelerator and integrates a supplied RISC-V core. A custom CPU, full ASIC or tapeout is not a core outcome.

Is this about using AI to write chip-design code?+

The main subject is hardware that executes AI computations. AI-assisted EDA is a different topic.

Is an accelerator speedup guaranteed?+

No. A correct design may be limited by transfers or control overhead. You are assessed on correctness, sound measurement and explanation of the observed result.

Will I build a large-language-model accelerator?+

The assessed workload is a small quantized kernel or compact model component that fits the qualified teaching platform.

Can graduates and working engineers both join?+

Yes, subject to the stated entry requirements. Both follow the same practical core, with preparation assigned through a diagnostic. Advanced pathways require the relevant foundations before their bridge.

How is the learning workload structured?+

The program combines live mentor-led classes, guided labs, project work and support sessions. An advisor can explain the current class format before enrollment.

Can I study online or on campus?+

RoboEdify offers its Hyderabad campus, live online classes and a working-professional format. Discuss the course-specific lab access and class format with an advisor.

What if I fall behind or need to pause?+

You can freeze your seat for up to 90 days and rejoin the next class at no extra fee. Saturday catch-up sessions and recordings of every live session support your learning. Recordings remain available for the lifetime of your account.

Is placement guaranteed?+

No. Career support includes portfolio and profile preparation, interview practice and role-fit introductions where available. RoboEdify does not guarantee an interview, offer, salary, employer, location or timeline.

What if I do not meet a practical requirement?+

Feedback identifies the missing capability and the work needed for reassessment. Attendance alone does not meet the proposed completion standard. Confirm course-specific reassessment arrangements before enrollment.

How can I learn about fees and lab access?+

Speak to a course advisor about the current offering, preparation requirements, fees, equipment or software access and learner-support terms.

Still have a question?

Find your starting point in AI Chip Design.
One million AI-native professionals by 2027.

Tell us about your technical background and the work you want to do. We’ll help you understand the preparation you need and how this course's projects connect to your learning goals.

Plan your learning

Course: AI Chip Design: RISC-V & AI Accelerator Engineering
Preparation: Diagnostic-based preparation before the common core
Level: Specialist
Curriculum: 11 modules

Confirm your intake dates, delivery mode, fees, assessment and practical access with RoboEdify before enrolling. Course content describes the learning scope; an enquiry does not reserve a seat.

AI Chip Design RISC-V & AI Accelerator Engineering

What will you design in an AI chip-design course?

More compute only helps when the rest of the system can feed it.

Learn through classes, labs and individual feedback.

A common core, with preparation matched to your starting point.

Leave with skills you can demonstrate.

What you practise before the core.

11 modules. A complete path from foundations to an independent capstone.

Workload definition and acceptance

Quantization and numeric correctness

RISC-V execution and platform integration

MAC datapath and pipeline design

Dataflow, buffers and bandwidth

Accelerator control and bus interface

Firmware and software reference path

Verification and stress testing

FPGA implementation and performance

Independent accelerator capstone

Interview and architecture defense

Use a focused stack to build and explain your work.

Three guided projects, followed by an independent capstone.

Quantized numeric reference

Parameterized MAC engine

RISC-V-controlled accelerator peripheral

Int8 matrix-vector accelerator on a RISC-V/FPGA platform

Demonstrate what you can do.

Prepare for relevant roles with work you can explain.

Build your portfolio. Prepare your profile. Practise your interviews.

Evidence from your own work.

Profile and resume preparation.

Interview practice and introductions.

Meet the team behind RoboEdify.

What employers say about RoboEdify’s AI and robotics graduates.

Meet alumni featured in our AI programs.

Come chat with us—on campus or online.

Support when you need to catch up.

Questions about prerequisites, tools and completion.

Find your starting point in AI Chip Design.One million AI-native professionals by 2027.

Plan your learning

Find your starting point in AI Chip Design.
One million AI-native professionals by 2027.