A tailored course, built for your situation
Deeper command of the AI hardware co-design framework
Name the patterns, decisions, and trade-offs that define leading-edge AI silicon strategy
$199 one-time
24-hour access provisioning
30-day money-back guarantee
Hand-built implementation playbook
12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
The situation this course is for
Who this is for
Senior technical leader shaping AI hardware strategy at a global research organization
Who this is not for
Engineers looking for hands-on RTL or physical design training, or managers seeking high-level trend summaries
What you walk away with
- Internalize the core decision loops of AI hardware co-design: where to optimize, where to accept trade-offs, and how to evaluate them
- Name the recurring patterns in workload-characteristic alignment across training, inference, and edge deployment
- Apply a standardized evaluation framework to compare architectural approaches before tapeout
- Guide cross-functional teams with a shared language rooted in first-principles reasoning
- Produce decision dossiers that stand up to peer and executive scrutiny without rework
The 12 modules (with all 144 chapters)
Module 1. Defining the co-design boundary
Clarify where AI workload requirements end and hardware constraints begin, and where they overlap. Map the key decision zones that require joint ownership between software, systems, and silicon teams.
12 chapters in this module
- What is co-design, really?
- Three boundaries to define upfront
- Workload vs. architecture ownership
- The integration accountability gap
- When hardware leads, when software leads
- Mapping latency-sensitive decisions
- Power envelope decision points
- Memory hierarchy trade-off zones
- On-chip vs. off-chip data flow
- Defining the shared success metric
- Avoiding premature optimization
- Setting the co-design charter
Module 2. The workload characterization stack
Break down AI workloads into measurable, design-relevant features. Move beyond 'model size' to tensor flow patterns, sparsity profiles, and dynamic execution paths that drive architecture choices.
12 chapters in this module
- From model to execution graph
- Token-level vs. batch dynamics
- Sparsity: natural vs. induced
- Control flow irregularity
- Memory access anti-patterns
- Operator frequency heatmaps
- Temporal locality scoring
- Data movement volume bands
- Kernel fusion feasibility index
- Workload-specific bottlenecks
- Benchmarking beyond throughput
- Creating the characterization dossier
Module 3. Hardware constraint modeling
Translate physical and economic constraints into quantifiable design boundaries. Model power, area, yield, and cost as first-order variables in co-design decisions.
12 chapters in this module
- Power envelope decomposition
- Thermal design power allocation
- Area budgeting by subsystem
- Yield impact of large dies
- Cost drivers in advanced nodes
- Packaging and interconnect limits
- Cooling system constraints
- Manufacturing process variability
- Voltage-frequency sweet spots
- Reliability under sustained load
- Testability and debug access
- Constraint prioritization matrix
Module 4. Decision pattern recognition
Identify the recurring choices that define AI silicon: tiling strategies, dataflow architectures, precision allocation, and memory hierarchy design. Recognize when a decision is novel versus a known pattern.
12 chapters in this module
- Tiling: spatial vs. temporal
- Dataflow taxonomy: systolic and beyond
- Precision: fixed, float, block, sparse
- On-chip memory hierarchy design
- Bandwidth vs. capacity trade-offs
- Interconnect topology selection
- Core count vs. clock speed
- Heterogeneous compute partitioning
- Compiler-aware microarchitecture
- Checkpointing and recovery design
- Error handling at scale
- Pattern-matching decision logs
Module 5. Evaluation framework design
Build a consistent scoring system for comparing architectural options. Define weighted criteria, normalize metrics, and surface hidden trade-offs before commitment.
12 chapters in this module
- Defining evaluation criteria
- Weighting performance vs. power
- Normalization across workloads
- Scoring design flexibility
- Risk of future obsolescence
- Time-to-adjustment metric
- Ease of software porting
- Compiler support assessment
- Benchmark suite selection
- Simulation fidelity requirements
- Cost per inference at scale
- Final go/no-go checklist
Module 6. Cross-functional alignment mechanics
Align software, systems, and hardware teams around shared decision rules. Replace negotiation with structured input, defined ownership, and clear escalation paths.
12 chapters in this module
- Ownership by decision type
- Input requirements per team
- Decision review cadence
- Conflict resolution protocol
- Documentation standards
- Versioning decision records
- Feedback loop timing
- Toolchain interoperability
- Shared simulation environments
- Joint calibration sessions
- Escalation triggers
- Stakeholder sign-off workflow
Module 7. Trade-off articulation
Communicate why a choice was made, not just what was chosen. Frame trade-offs in terms of workload impact, hardware cost, and long-term flexibility.
12 chapters in this module
- The cost of not optimizing
- Opportunity cost of silicon area
- Performance cliff identification
- Future-proofing vs. over-engineering
- Software maintainability cost
- Debug complexity penalty
- Thermal throttling risk
- Yield-reliability trade-off
- Precision degradation impact
- Scalability ceiling analysis
- Support lifecycle implications
- Trade-off communication template
Module 8. First-principles silicon reasoning
Develop the ability to reason from physics and math to architectural choices, bypassing vendor claims or trend-following. Apply first-principles thinking to novel problems.
12 chapters in this module
- From transistor physics to throughput
- Amdahl’s Law in AI systems
- Little’s Law and queueing theory
- Landauer’s principle relevance
- Energy per operation floor
- Bandwidth-delay product impact
- Pipelining efficiency limits
- Parallelism overhead calculation
- Cache hit rate sensitivity
- Memory wall quantification
- Compute-utilization gap
- First-principles validation checklist
Module 9. Decision traceability systems
Ensure every architecture choice can be traced to workload data, constraints, and evaluation results. Build auditable, updatable decision records.
12 chapters in this module
- Decision input provenance
- Workload data versioning
- Constraint update tracking
- Evaluation score audit trail
- Alternative option logs
- Rejection rationale capture
- Assumption change detection
- Sensitivity analysis documentation
- Cross-module impact mapping
- Automated traceability checks
- Decision lineage visualization
- Living decision repository
Module 10. Avoiding consensus-driven drift
Prevent architectural compromises that satisfy all teams but optimize for none. Identify and eliminate weak consensus outcomes before implementation.
12 chapters in this module
- The myth of full alignment
- Identifying lowest-common-denominator design
- Stakeholder over-representation
- Time-pressure compromises
- Defaulting to legacy patterns
- Fear of ownership avoidance
- Measuring decision strength
- Conflict as signal, not noise
- Strong vs. weak consensus
- Ownership clarity test
- Revisiting deferred decisions
- Consensus anti-patterns
Module 11. Scaling co-design across projects
Replicate proven co-design practices across multiple teams and initiatives. Adapt the framework without diluting its rigor or consistency.
12 chapters in this module
- Template vs. playbook distinction
- Tailoring without weakening
- Common language adoption
- Cross-team calibration
- Mentorship model design
- Peer review integration
- Standardized documentation
- Tooling commonality
- Shared learning repository
- Feedback aggregation system
- Scaling decision velocity
- Institutionalizing the framework
Module 12. Leading without direct authority
Exert influence across silos by mastering the framework and demonstrating superior decision clarity. Become the go-to reference for co-design excellence.
12 chapters in this module
- Credibility through consistency
- Decision clarity as influence
- Pre-mortem facilitation
- Hosting design deep dives
- Asynchronous decision reviews
- Pre-submission alignment
- Building trusted advisor status
- Documented reasoning over opinion
- Invitation to lead expansions
- Shaping org-wide standards
- Mentoring next-tier leads
- Establishing technical authority
How this maps to your situation
- Defining the co-design boundary
- The workload characterization stack
- Hardware constraint modeling
- Decision pattern recognition
Before vs. after
Before
Relying on experience and consensus to guide AI hardware decisions, with inconsistent documentation and variable team alignment.
After
Applying a rigorous, repeatable framework to co-design decisions, producing clear, defensible, and scalable outcomes across teams and projects.
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters)
- Downloadable templates and worked examples for every module
- Hand-built implementation playbook delivered alongside course access
- 30-day money-back guarantee
Delivery and format
- Course and learning environment access provisioned within 24 hours of purchase
- Hand-built implementation playbook delivered alongside course access
Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.
Time investment: Approximately 3 hours per module, or 36 hours total, designed for completion over 6, 8 weeks with spaced application.
How this compares to the alternatives
Most AI hardware courses focus on circuit design, RTL, or high-level trends. This course is unique in targeting the decision framework used by senior leaders to align software, systems, and silicon, without getting lost in implementation details or vague strategy.
Frequently asked
Is this course about chip design or physical implementation?
No. This course focuses on the decision framework for AI hardware co-design, not physical design, RTL, or fabrication processes.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will I receive support during the course?
The course is self-paced with detailed templates and examples. No live support is provided.
$199 one-time. Approximately 3 hours per module, or 36 hours total, designed for completion over 6, 8 weeks with spaced application..
Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.
30-day money-back guarantee·
144 chapters·
Hand-built playbook included·
Account access within 24 hours