Game Days for AI-Assisted Chip Design: Testing Resilience Before Tape-Out

Game days provide a structured approach to validate fault tolerance in AI-augmented EDA pipelines. This post provides a practical framework for testing resilience patterns under controlled conditions.

Mario Sageth

Sr. Technical Account Manager

Published Jun 2, 2026

Last modified Jun 2, 2026

Introduction

Semiconductor teams invest significant effort building resilient infrastructure — multi-model fallback, checkpoint/restart for long-running jobs, license server failover, and cross-AZ compute distribution. But most never validate these patterns under realistic failure conditions until a real incident forces the question.

The consequences of untested resilience in Electronic Design Automation (EDA) are uniquely severe. A tape-out deadline doesn't move because your failover didn't work as expected. A 72-hour place-and-route job that loses progress due to an untested checkpoint mechanism costs days of schedule — and potentially a foundry submission window.

Game days — structured exercises that inject controlled failures (sometimes called chaos engineering exercises) — bridge the gap between "we designed for resilience" and "we know it works." This post provides a practical framework for testing fault tolerance in AI-augmented EDA pipelines on AWS, covering AI inference degradation, compute interruptions, storage degradation, and license server failover.

Prerequisites

Before running game day exercises, ensure the following are in place:

AWS Fault Injection Service (FIS) — IAM role with permissions to target your EDA resources (EC2, EBS, Amazon FSx, VPC endpoints, SSM)
Amazon CloudWatch alarms — configured for job failure rate, license checkout failures, and storage latency thresholds
Checkpoint mechanism — verified that your EDA tools (Synopsys, Cadence, Siemens) are configured to write periodic checkpoints to Amazon S3 or Amazon FSx
License server failover — standby instance pre-configured with Elastic Network Interface (ENI) or triad architecture in place
Non-production environment — initial tests should run against a non-production EDA cluster with representative workloads
Stakeholder notification plan — communication template for engineering leadership and affected design teams
Rollback procedure — documented steps to stop FIS experiments immediately if impact exceeds expectations

Cost consideration: Running game day exercises incurs charges for FIS experiments, compute instances, and any EDA license time consumed during testing. Estimated cost per game day: $50–$200 depending on cluster size and duration. Review the Resource Cleanup section after each exercise.

EDA-Specific Failure Domains

AI-augmented EDA pipelines have more failure domains than typical cloud applications. Each domain requires targeted testing:

Failure Domain	What Can Go Wrong	EDA Impact
AI inference	Amazon Bedrock throttling, model degradation, endpoint unavailability	Design rule queries fail, Register Transfer Level (RTL) generation stalls, verification debugging unavailable
Compute	Spot interruptions mid-synthesis, Availability Zone (AZ) loss, node failures	Long-running jobs lose progress, job queues back up
Storage	Amazon FSx latency spikes, Amazon S3 replication lag, checkpoint corruption	EDA tools stop responding on I/O, checkpoint/restart fails, data inconsistency
License servers	Primary server down, failover timing, license exhaustion during peak	Hundreds of engineers blocked simultaneously, jobs queued indefinitely
Network	AWS Direct Connect flap, VPC endpoint degradation	On-premises ↔ cloud connectivity lost, Amazon Bedrock API unreachable
Compound	Multiple domains failing simultaneously	Cascading failures — the realistic scenario most teams never test

Each domain requires its own test scenario, success criteria, and recovery validation.

Game Day Framework

Cadence and Approach

Run game days quarterly, timed to avoid active tape-out sprints. Start in non-production environments, then graduate to controlled production tests once confidence is established.

Pre-Game Day Checklist

Define success criteria — what does "pass" look like for each scenario? (Example: "Jobs resume from checkpoint within 15 minutes of Spot interruption")
Set blast radius controls — limit the scope of injected failures (single AZ, subset of compute, one model tier)
Notify stakeholders — engineering leadership and affected design teams know the test is happening
Instrument observability — confirm dashboards, alarms, and logging are capturing the metrics you need
Prepare rollback — know how to stop the experiment immediately if impact exceeds expectations

The Five Core Scenarios

#	Scenario	Tests	Expected Outcome
01	Spot interruption mid-synthesis	Checkpoint/restart, job requeue	Jobs resume from last checkpoint within Recovery Time Objective (RTO)
02	AI inference endpoint failure	Circuit breaker, model fallback chain	Fallback activates within seconds; design queries continue at degraded quality
03	License server failover	Secondary takeover, license re-acquisition	Running jobs retain licenses; new checkouts resume within RTO
04	Storage degradation	EDA tool I/O handling, job timeout behavior	Tools handle slow storage gracefully; alerts fire before jobs fail
05	Cross-AZ failure	Compute redistribution, storage failover	AWS Batch redistributes work; no single-AZ dependency causes full outage
06	Retrieval-Augmented Generation (RAG) Knowledge Base degradation	Confidence scoring, grounding checks, staleness detection	Assistant refuses to answer when confidence is low; no hallucinated design rules

Scenario 1: Spot Interruption Mid-Synthesis

What You're Testing

Long-running EDA jobs (synthesis, place-and-route, formal verification) can run for hours or days. Spot Instances provide significant cost savings but can be reclaimed with 2 minutes notice. This test validates that your checkpoint mechanism actually works under interruption.

AWS Fault Injection Service (FIS) Experiment

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{
  "description": "Terminate 30% of Spot Instances in EDA compute environment",
  "targets": {
    "spotInstances": {
      "resourceType": "aws:ec2:spot-instance",
      "selectionMode": "PERCENT(30)",
      "resourceTags": {
        "workload": "eda-synthesis"
      }
    }
  },
  "actions": {
    "interruptSpot": {
      "actionId": "aws:ec2:send-spot-instance-interruptions",
      "parameters": {
        "durationBeforeInterruption": "PT2M"
      },
      "targets": {
        "SpotInstances": "spotInstances"
      }
    }
  },
  "stopConditions": [
    {
      "source": "aws:cloudwatch:alarm",
      "value": "arn:aws:cloudwatch:us-west-2:123456789012:alarm:eda-job-failure-rate-critical"
    }
  ]
}

Note: The durationBeforeInterruption parameter simulates the real-world 2-minute warning. The actual interruption behavior (terminate, stop, or hibernate) is governed by the Spot Instance request's configured InstanceInterruptionBehavior — not by the FIS action itself.

Validation Criteria

Interrupted jobs write checkpoint to Amazon S3 or Amazon FSx within the 2-minute warning window
Job scheduler (AWS Batch or Slurm) detects termination and requeues affected jobs
Requeued jobs resume from the last checkpoint — not from the beginning
Time from interruption to resumed execution is within RTO (target: <15 minutes)
No data corruption in checkpoint files (verify with integrity checks post-resume)

Scenario 2: AI Inference Degradation

What You're Testing

When Amazon Bedrock is throttled or unavailable, your AI-assisted workflows (design rule queries, RTL generation, verification debugging) should degrade gracefully — not fail hard and block engineers. You also validate that Amazon Bedrock Guardrails continue to enforce grounding checks even when fallback models are active.

FIS Experiment — Disrupt Amazon Bedrock VPC Endpoint Directly

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{
  "description": "Block traffic to Amazon Bedrock VPC endpoint by replacing its security groups",
  "targets": {
    "bedrockEndpoint": {
      "resourceType": "aws:ec2:vpc-endpoint",
      "resourceArns": [
        "arn:aws:ec2:us-west-2:123456789012:vpc-endpoint/vpce-0abc123bedrockeni"
      ],
      "selectionMode": "ALL"
    }
  },
  "actions": {
    "blockBedrock": {
      "actionId": "aws:network:disrupt-vpc-endpoint",
      "parameters": {
        "duration": "PT5M"
      },
      "targets": {
        "VpcEndpoints": "bedrockEndpoint"
      }
    }
  },
  "stopConditions": [
    {
      "source": "aws:cloudwatch:alarm",
      "value": "arn:aws:cloudwatch:us-west-2:123456789012:alarm:ai-inference-total-failure"
    }
  ]
}

Note: The aws:network:disrupt-vpc-endpoint action blocks all inbound and outbound traffic by replacing the endpoint's security groups with an empty FIS-managed security group. This is the most targeted approach — it isolates disruption to the Amazon Bedrock endpoint without affecting other subnet traffic. Upon experiment completion, the original security groups are restored automatically.

Validation Criteria

Circuit breaker trips within 5 consecutive failures (~10 seconds)
Fallback model chain activates (primary → fallback → economy)
Semantic cache serves responses for previously-seen queries
Novel queries receive a clear "service degraded" message — not a hallucinated answer
Guardrails continue to block ungrounded responses even during fallback model activation
Recovery is automatic when connectivity returns (circuit breaker enters half-open state)
Engineers are notified of degraded AI capability (Slack alert, dashboard banner)

Scenario 3: License Server Failover

What You're Testing

EDA license servers are a single point of failure that can block hundreds of engineers simultaneously. This test validates that your failover mechanism (Elastic Network Interface detach/attach, DNS update, or triad quorum) works within acceptable time.

Test Procedure

Note: While FIS provides both aws:ec2:stop-instances (reversible) and aws:ec2:terminate-instances (irreversible) actions, this scenario uses direct CLI for simplicity and to simulate an abrupt, unannounced failure — closer to a real hardware crash than a managed experiment. For repeatable quarterly testing, consider wrapping this in an FIS experiment using aws:ec2:terminate-instances with appropriate stop conditions.

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# Simulate primary license server failure
# (Terminate the instance — ENI persists independently)
aws ec2 terminate-instances \
  --instance-ids i-0abc123def456789 \
  --region us-west-2

# Monitor: How long until secondary takes over?
# Measure: lmstat connectivity from compute nodes
watch -n 5 "lmstat -a -c 27000@license-server.eda.internal"

Validation Criteria

Secondary license server detects primary failure (health check or heartbeat)
ENI (with MAC-bound license) attaches to standby instance within RTO
DNS record updates (Amazon Route 53 health check failover)
Running jobs retain their checked-out licenses during the transition
New license checkouts succeed within 2 minutes of failover completion
No mass job termination due to license connectivity loss

Scenario 4: Storage Degradation

What You're Testing

EDA tools are extremely I/O-sensitive. A latency spike on Amazon FSx for Lustre or Amazon EBS can cause tools to stop responding, timeout, or produce corrupted output. This test validates that your pipeline handles slow storage gracefully rather than silently producing bad results.

FIS Experiment — EBS Volume IO Latency Injection

For EBS-backed storage (local scratch, checkpoint volumes):

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{
  "description": "Inject 500ms IO latency on 50% of read/write operations to EDA compute EBS volumes",
  "targets": {
    "edaVolumes": {
      "resourceType": "aws:ebs:volume",
      "resourceTags": {
        "workload": "eda-compute"
      },
      "selectionMode": "PERCENT(50)"
    }
  },
  "actions": {
    "injectLatency": {
      "actionId": "aws:ebs:volume-io-latency",
      "parameters": {
        "duration": "PT5M",
        "ioLatencyMilliseconds": "500",
        "ioLatencyPercentage": "50",
        "ioLatencyPattern": "sustained"
      },
      "targets": {
        "Volumes": "edaVolumes"
      }
    }
  }
}

Note: The aws:ebs:volume-io-latency action (released September 2025) supports configurable latency patterns: sustained, increasing, intermittent, and decreasing — enabling realistic degradation scenarios rather than binary pass/fail.

Amazon FSx for Lustre Workaround

No Amazon FSx for Lustre-specific FIS actions exist as of May 2026. To simulate Lustre storage degradation, use network-level disruption between compute nodes and the Lustre file system endpoints:

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{
  "description": "Disrupt network path between EDA compute and Amazon FSx for Lustre (AZ-scoped)",
  "targets": {
    "computeSubnets": {
      "resourceType": "aws:ec2:subnet",
      "resourceTags": {
        "role": "eda-compute"
      },
      "selectionMode": "ALL"
    }
  },
  "actions": {
    "disruptLustrePath": {
      "actionId": "aws:network:disrupt-connectivity",
      "parameters": {
        "duration": "PT5M",
        "scope": "availability-zone"
      },
      "targets": {
        "Subnets": "computeSubnets"
      }
    }
  }
}

Validation Criteria

EDA tools detect slow I/O and log warnings (not silent unresponsiveness)
Job completion time monitoring alerts fire (outlier detection)
No data corruption in output files despite elevated latency
Checkpoint writes complete successfully (may be slower but not failed)
After latency injection ends, tools recover without manual intervention

Scenario 5: Cross-AZ Failure

What You're Testing

A full Availability Zone failure affects compute, storage, and potentially license servers simultaneously. This test validates that your architecture has no single-AZ dependencies that cause a complete outage.

FIS Experiment — AZ Disruption

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{
  "description": "Simulate AZ failure for EDA workloads",
  "targets": {
    "azInstances": {
      "resourceType": "aws:ec2:instance",
      "selectionMode": "ALL",
      "filters": [
        {
          "path": "Placement.AvailabilityZone",
          "values": ["us-west-2a"]
        }
      ],
      "resourceTags": {
        "workload": "eda"
      }
    }
  },
  "actions": {
    "stopAZ": {
      "actionId": "aws:ec2:stop-instances",
      "parameters": {
        "startInstancesAfterDuration": "PT10M"
      },
      "targets": {
        "Instances": "azInstances"
      }
    }
  }
}

Validation Criteria

AWS Batch redistributes pending jobs to surviving AZs
Amazon FSx for Lustre note: SSD and HDD file systems are single-AZ by design. (Intelligent-Tiering file systems replicate across AZs but are less common for latency-sensitive EDA workloads.) If the file system is in the affected AZ, validate that jobs can access data via S3-backed hydration or a replica file system in another AZ. For Multi-AZ storage resilience, consider Amazon FSx for NetApp ONTAP.
License server (if in affected AZ) fails over to standby in another AZ
AI inference (Amazon Bedrock via VPC endpoint) remains available — endpoints are Regional, not AZ-scoped
Total capacity reduction is proportional (lose 1/3, not 100%)
Recovery is automatic when AZ returns — no manual intervention required

Scenario 6: RAG Knowledge Base Degradation

What You're Testing

When the Knowledge Base returns partial, stale, or irrelevant results, the AI assistant may hallucinate incorrect design rules — arguably the most dangerous failure mode because it produces plausible but wrong answers rather than obvious errors.

Test Procedure

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# Inject latency into the OpenSearch/vector store backing the Knowledge Base
# This simulates partial retrieval (timeouts on some chunks)
aws fis start-experiment \
  --experiment-template-id EXT-rag-latency-injection \
  --region us-west-2

# Alternatively: temporarily point the Knowledge Base at a stale S3 data source
# (copy of Process Design Kit (PDK) docs from 2 versions ago) to test staleness detection

Validation Criteria

Confidence scoring detects low-relevance retrieval results (score < 0.70)
Assistant refuses to answer design rule queries when confidence is below threshold — returns "consult PDK manual" instead of hallucinating
Guardrails contextual grounding check blocks ungrounded responses
Staleness detection fires if Knowledge Base index is outdated vs. current PDK version
Fallback to keyword search activates when vector search times out
Engineers receive a clear warning: "Knowledge Base degraded — verify answers against PDK"

This scenario is critical because a wrong design rule (e.g., incorrect minimum spacing) can cause silicon failure. An assistant that says "I don't know" is safer than one that confidently provides incorrect constraints.

Observability During Game Days

What to Instrument Before Testing

Ensure these are in place before running any game day:

Metric	Purpose	Source
Job completion rate	Detect if jobs are failing or stalling	AWS Batch / Slurm metrics → Amazon CloudWatch
AI inference fallback rate	Confirm circuit breaker is working	Gateway custom metrics → CloudWatch
License checkout success rate	Detect license server issues	FlexLM/SALT logs → CloudWatch
Storage I/O latency (P99)	Detect Amazon FSx degradation	Amazon FSx CloudWatch metrics
Time-to-recovery per scenario	Measure actual RTO	CloudWatch event timestamps (interruption → recovery)

Post-Game-Day Analysis

After each game day, document:

What worked — which resilience patterns activated correctly?
What didn't — where did recovery take longer than expected or fail entirely?
Surprises — unexpected cascading effects or dependencies discovered?
Action items — specific fixes with owners and deadlines
Updated RTO targets — adjust based on measured reality vs. assumptions

Building a Resilience Scorecard

Track improvement over time with a simple scorecard:

Scenario	Q1 Result	Q2 Result	Q3 Result	Target
Spot interruption recovery	22 min	14 min	—	<15 min
AI inference failover	45 sec	12 sec	—	<30 sec
License server failover	8 min	3 min	—	<5 min
Storage degradation handling	FAIL (silent unresponsiveness)	PASS (alert fired)	—	Alert + graceful
Cross-AZ redistribution	12 min	6 min	—	<10 min
RAG degradation handling	FAIL (hallucinated)	PASS (refused + warned)	—	Refuse when low confidence

Tie the scorecard to tape-out readiness criteria: "No tape-out proceeds unless all six scenarios pass within target RTO in the most recent game day."

AWS Well-Architected alignment: Game days directly implement the Reliability Pillar's guidance on testing recovery procedures (REL13-BP01) and running game days regularly (REL13-BP03). For GenAI-specific resilience patterns, refer to the AWS Well-Architected Generative AI Lens .

Resource Cleanup

After completing game day exercises, remove or stop the following resources to avoid ongoing charges:

Stop FIS experiments — verify all experiments show status Completed or Stopped in the FIS console
Terminate test instances — any additional EC2 instances launched for game day validation
Review FIS experiment templates — archive or version for quarterly reuse rather than deleting
Review CloudWatch alarms — disable or delete game-day-specific alarms that are no longer needed
Clear test checkpoints — remove checkpoint files written to Amazon S3 during Spot interruption testing (verify no production data before deletion)
Restore security group rules — if any network rules were modified for Scenario 2 (Amazon Bedrock endpoint blocking)
Verify license server state — confirm primary license server is restored and serving after Scenario 3 testing

⚠️ Data loss warning: Do not delete Amazon S3 checkpoint buckets or Amazon FSx file systems without confirming they contain only test data. Production EDA design data may share storage paths.

To verify no orphaned FIS resources remain:

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# List active FIS experiments
aws fis list-experiments --query "experiments[?state.status=='running']"

# Verify no lingering SSM documents from latency injection
aws ssm list-commands --filters "key=Status,value=InProgress"

Conclusion

Resilience is a practice, not a feature. Patterns that aren't tested degrade silently — configuration drift, software updates, and infrastructure changes erode fault tolerance over time without anyone noticing.

Start with one game day. Pick the scenario that scares you most (for most EDA teams, it's Spot interruption mid-synthesis or license server failover). Run it in a non-production environment. Measure the actual recovery time. Fix what breaks. Then expand quarterly until all five scenarios pass consistently.

The semiconductor companies that ship on schedule aren't the ones that never experience failures — they're the ones that have already practiced recovering from them.

# resilience # chaos-engineering # aws-game-day # generative-ai

Any opinions in this article are those of the individual author and may not reflect the opinions of AWS.

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Builder Center

Game Days for AI-Assisted Chip Design: Testing Resilience Before Tape-Out

Mario Sageth

Introduction

Prerequisites

EDA-Specific Failure Domains

Game Day Framework

Cadence and Approach

Pre-Game Day Checklist

The Five Core Scenarios

Scenario 1: Spot Interruption Mid-Synthesis

What You're Testing

AWS Fault Injection Service (FIS) Experiment

Validation Criteria

Scenario 2: AI Inference Degradation

What You're Testing

FIS Experiment — Disrupt Amazon Bedrock VPC Endpoint Directly

Validation Criteria

Scenario 3: License Server Failover

What You're Testing

Test Procedure

Validation Criteria

Scenario 4: Storage Degradation

What You're Testing

FIS Experiment — EBS Volume IO Latency Injection

Amazon FSx for Lustre Workaround

Validation Criteria

Scenario 5: Cross-AZ Failure

What You're Testing

FIS Experiment — AZ Disruption

Validation Criteria

Scenario 6: RAG Knowledge Base Degradation

What You're Testing

Test Procedure

Validation Criteria

Observability During Game Days

What to Instrument Before Testing

Post-Game-Day Analysis

Building a Resilience Scorecard

Resource Cleanup

Conclusion