{"resourceId":"jetson-orin-memory-clock-tail-study-260616106","versions":[{"version":"external-ff275ace98ee42c6ff2fdfb548937637be00404295749e9572dc1ac7bda556e8","resource":{"id":"jetson-orin-memory-clock-tail-study-260616106","title":"Jetson study finds memory settings and miss bursts can defeat latency estimates","organization":"Jaehoon Kang, Cleinsoft","sector":"Edge inference systems research","geography":"Experimental board study; deployment jurisdiction unspecified","publishedAt":"June 15, 2026 preprint v1","publicationDate":"2026-06-15","eventDate":null,"sourceName":"arXiv","sourceLabel":"Industry-affiliated research preprint independent of NVIDIA authorship","sourceUrl":"https://arxiv.org/html/2606.16106v1","evidenceClass":"independent-research","outcomeClass":"cautionary","topics":["infrastructure","developers-agents","operating-model"],"finding":"A Cleinsoft-authored preprint finds memory-clock sensitivity and clustered deadline misses on one Jetson board.","sledRelevance":"Interpretation: Relevant to teams evaluating edge inference under time constraints, including university engineering labs. It does not establish an operational SLED failure or justify deployment in a life-safety workflow.","evidence":"One Orin Nano Super ran six workloads across four memory-clock settings; the tail study used eight 100,000-cycle cells. A GPU-only fit evaluated at 2133 MHz after profiling at 3199 MHz had maximum latency underestimation of 32.2% for the decode proxy (Table III). The comparison changes memory state, not hardware.","architectureImplications":"Interpretation: include memory state and competing activity in local profiling; preserve sequence-level deadline records.","governanceImplications":"Interpretation: require application owners to define tolerated missed-deadline patterns before selecting a device.","securityPrivacyImplications":"Interpretation: isolate competing workloads and protect device administration; this performance study does not validate security controls.","caveats":"Single board, selected workloads and streaming-write contention; mechanisms unresolved. No independent replication here, no measured power, and no transfer of effect sizes to data-center GPUs or NIM.","streamIds":["nvidia"],"roles":{"sales":"Interpretation: The customer problem is an edge application that sometimes responds too late despite reassuring average performance. Engage the application owner, embedded engineers, operations and procurement. Ask how consecutive late results affect the workflow, what other processes share the device and whether the operating configuration changes after installation. Offer a bounded timing assessment on a representative noncritical workload. The value hypothesis is discovering deployment constraints before committing to a fleet. This study supports asking better validation questions; it does not establish a failure rate for the customer's device, a NVIDIA-wide defect, energy savings or suitability for safety-critical use.","engineering":"Interpretation: Reproduce the intended workload on the actual board and approved software before adjusting a latency model. Record CPU, GPU and memory settings, thermal conditions and competing processes with timing traces. Require safe test isolation and authorized device administration; do not copy experimental privileged settings into production. Compare the intended configuration against a controlled baseline, then vary plausible interference. Evaluate both aggregate latency and sequences of missed deadlines. A useful proof of value identifies whether the application's tolerance is exceeded and which variables remain untested. Validate data handling independently; this experiment cannot certify isolation or confidentiality.","delivery":"Interpretation: The embedded service owner should coordinate application developers, device maintainers and operational users. Implement reproducible images, configuration inventory, timing capture and a rollback procedure. Dependencies include representative hardware, test fixtures and staff able to interpret scheduler and runtime behavior. Train users to recognize stale results and invoke an approved fallback. Governance checkpoints should precede fleet rollout and material firmware changes. Proposed acceptance criteria are successful replay of the agreed workload, documented miss-pattern limits and a demonstrated fallback under induced delay. Risks include untested co-runners, device variation and extrapolating a laboratory result beyond its measured environment."},"retrievedAt":"2026-09-09T03:00:55Z","enrichedAt":"2026-09-09T03:04:36Z","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Interpretation: no accessibility outcome is established. Operators need embedded-system profiling skills and usable failure indicators.","procurementImplications":"Interpretation: require representative-device tests under the proposed power and software configuration.","operatingModelImplications":"Interpretation: assign responsibility for firmware, runtime and power-profile changes and their revalidation.","updateExplanation":"Exact paper identifier and Jetson searches returned no archive match. June research is newly covered scrutiny of inference assumptions, not a September event.","sourceVerification":{"openedUrl":"https://arxiv.org/html/2606.16106v1","referenceExcerpt":"Every measurement in this paper came from one Jetson Orin Nano Super.","promptVersion":"sled-research-v3.1","model":null,"basis":"agent-reported inspection"}}}]}