BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Silicon Valley Engineering Council - ECPv6.18.0//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Silicon Valley Engineering Council
X-ORIGINAL-URL:https://svec.org
X-WR-CALDESC:Events for Silicon Valley Engineering Council
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20250309T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20251102T090000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20260308T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20261101T090000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20270314T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20271107T090000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20261013T080000
DTEND;TZID=America/Los_Angeles:20261014T180000
DTSTAMP:20260714T183313Z
CREATED:20260714T183313Z
LAST-MODIFIED:20260714T183313Z
UID:78655-1791878400-1792000800@svec.org
SUMMARY:Minimalist Edge AI Student Competition
DESCRIPTION:Part of the 11th ACM/IEEE Symposium on Edge Computing (SEC 2026) Location: Santa Clara University | Date: Oct 13-15\, 2026\nOverview\nThe future of the Edge is not just decentralized—it must be sustainable. As AI models grow in complexity\, the environmental and energetic costs of deploying them on edge devices have become a critical bottleneck.\nThe Minimalist Edge AI Challenge invites student teams to tackle the "AI Bloat" head-on. This one-day competition challenges you to take state-of-the-art "heavy" models and transform them into lean\, green\, high-performance machines capable of running on constrained\, eco-friendly hardware. Can you maintain intelligence while slashing the carbon footprint? Join our Discord group for latest updates (https://discord.gg/V29ZXCTFR5).\n—————————————————————\nThe Challenge\nParticipating teams will be tasked with deploying a complex AI workload (e.g.\, real-time multi-object tracking or environmental anomaly detection) onto provided low-power hardware (Qualcomm Arduino UNO Q).\nYour mission is to optimize for the "Green Trifecta":\n–\nModel Accuracy: Maintain high predictive performance and reliability while operating under strict resource constraints.\n–\nModel Compression: Use pruning\, quantization\, and distillation to shrink the model footprint.\n–\nResource Efficiency: Minimize peak RAM and CPU utilization to ensure smooth execution on minimalist hardware.\n–\nEnergy Parsimony: Achieve the lowest possible Joules-per-inference to maximize battery life and sustainability.\n—————————————————————\nWhy Participate?\n–\nShowcase Your Skills: Pitch your solution to a jury of world-leading edge computing experts from academia and industry.\n–\nBuild Your Career & Network: Pitch your solution to a premier jury of world-leading edge computing experts and recruiters from academia and industry giants like Qualcomm.\n–\nEngineer for Planet Earth: Move past theoretical AI. Help define the actual benchmarks for "Green AI" and create real-world impact by building hyper-efficient\, eco-friendly edge computing models.\n–\nCash Prizes: The top three teams from each track will receive cash rewards:\n–\n1st Place: $1\,000\n–\n2nd Place: $750\n–\n3rd Place: $500\n–\nParticipant Perks: All competition participants will receive IEEE Computer Society swag and a free IEEE Computer Society Student Membership (Note: Requires an active IEEE Student Membership).\n—————————————————————\nEligibility & Team Composition\n–\nOpen to all undergraduate and graduate students registered at a recognized university.\n–\nTeams should consist of 2 to 4 members.\n–\nRecommended Skill Set: * Proficiency in Python (PyTorch/TensorFlow) and/or C++/Rust.\n–\nExperience with model optimization techniques (Pruning\, Quantization).\n–\nKnowledge of Linux systems and resource profiling.\n–\nA passion for sustainable engineering.\n—————————————————————\nImportant Dates\n● Team Registration Deadline: Sept. 6th\, 2026\n● Competition Day: Oct. 13-16 (Co-located with SEC 2026)\nCo-sponsored by: QualComm\n500 El Camino Real\, Santa Clara\, California\, United States\, 95053
URL:https://svec.org/event/minimalist-edge-ai-student-competition/
LOCATION:500 El Camino Real\, Santa Clara\, California\, United States\, 95053
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20261013T080000
DTEND;TZID=America/Los_Angeles:20261016T180000
DTSTAMP:20261005T024805Z
CREATED:20261005T024805Z
LAST-MODIFIED:20261005T024805Z
UID:78980-1791878400-1792173600@svec.org
SUMMARY:ACM/IEEE Symposium on Edge Computing
DESCRIPTION:ACM/IEEE SEC 2026 and the 14th IEEE International Conference on Cloud Engineering (IC2E 2026) will be held together at Santa Clara University. Attendees may participate in technical sessions from either conference\, and both communities will come together for a joint poster and demo session.\nSEC is a forum for top researchers\, engineers\, students\, entrepreneurs\, and government officials come together under one roof to discuss the opportunities and challenges that arise from rethinking cloud computing architectures and embracing edge computing. SEC takes a broad view of edge computing and solicits contributions from many fields of systems practice that embrace any aspect of edge computing.\nBldg: Benson Memorial Center\, 820 Alviso St\, \, Santa Clara University\, Santa Clara \, California\, United States\, 95050
URL:https://svec.org/event/acm-ieee-symposium-on-edge-computing/
LOCATION:Bldg: Benson Memorial Center\, 820 Alviso St\, \, Santa Clara University\, Santa Clara \, California\, United States\, 95050
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20261013T120000
DTEND;TZID=America/Los_Angeles:20261013T130000
DTSTAMP:20261008T024806Z
CREATED:20261008T024806Z
LAST-MODIFIED:20261008T024806Z
UID:78992-1791892800-1791896400@svec.org
SUMMARY:A Bit Personal Live: Jodi Shelton in Conversation with John Hennessy
DESCRIPTION:Stanford IEEE is hosting a live on-campus taping of "A Bit Personal\," the Global Semiconductor Alliance's podcast. GSA CEO Jodi Shelton sits down with John Hennessy\, Stanford President Emeritus and Chairman of Alphabet\, to talk about his career in computer architecture\, leading Stanford\, and the future of the semiconductor industry.\nTuesday\, October 13\, 12-1 PM. Free and open to Stanford students\, staff\, and faculty.\nDetails and RSVP: https://luma.com/x4vhq2eq\nCo-sponsored by: Global Semiconductor Alliance (GSA)\nRoom: 101X\, Bldg: Paul G. Allen Building\, 420 Via Palou\, Stanford\, California\, United States\, 94305
URL:https://svec.org/event/a-bit-personal-live-jodi-shelton-in-conversation-with-john-hennessy/
LOCATION:Room: 101X\, Bldg: Paul G. Allen Building\, 420 Via Palou\, Stanford\, California\, United States\, 94305
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20261013T190000
DTEND;TZID=America/Los_Angeles:20261013T210000
DTSTAMP:20260919T204819Z
CREATED:20260919T204819Z
LAST-MODIFIED:20260919T204819Z
UID:78924-1791918000-1791925200@svec.org
SUMMARY:Trusted AI Adoption Using Fine-Tuned Reinforcement Learning
DESCRIPTION:This is an online event. Pre-registration is required.\nTrusted adoption of an AI solution in an enterprise setting has many requirements. Large Language Models (LLMs) can often be constrained by the risks of hallucination bias and limited operational trust and reliability. Overcoming these challenges requires models whose output is fully grounded\, auditable\, and accurate. In addition\, enterprise policy compliancy is critical in highly regulated sectors such as finance\, healthcare\, legal\, insurance\, government\, and critical infrastructure.\nThis presentation will discuss a solution framework with open-source models that can be systematically adapted using a combination of post-training optimization with advanced and agentic Retrieval-Augmented Generation (RAG) to create a production-ready enterprise LLM architecture. Advanced RAG is able to strengthen factual grounding through hybrid retrieval\, metadata filtering\, reranking\, citation generation\, and context optimization. Agentic RAG is able to extend this capability by enabling planner\, retriever\, verifier\, and critic agents to decompose complex tasks\, iteratively retrieve evidence\, validate sufficiency\, re-query when needed\, and produce auditable answers suitable for enterprise production use.\nSpeaker(s): Prasad Venkatachar\,\nVirtual: https://events.vtools.ieee.org/m/577393
URL:https://svec.org/event/trusted-ai-adoption-using-fine-tuned-reinforcement-learning/
LOCATION:Virtual: https://events.vtools.ieee.org/m/577393
END:VEVENT
END:VCALENDAR