BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20270314T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20261101T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260929T011657Z UID:88D9FC23-4B97-4965-A1C0-0F2F0DC665A8 DTSTART;TZID=America/New_York:20261201T153000 DTEND;TZID=America/New_York:20261201T170000 DESCRIPTION:As deep learning models scale into multi-billion parameter spac es\, modern enterprise architectures face an immediate barrier: the memory bandwidth wall. Standard operator execution patterns bottleneck high-perf ormance systems by forcing intermediate tensor states to repeatedly cycle through high-bandwidth memory (HBM). This lecture addresses the mathematic al and hardware codesign patterns required to achieve sublinear search man dates and drive maximum runtime execution throughput across real-time\, mu lti-modal environments. Through a deep dive into PyTorch implementation me chanics\, we examine the dual paradigms of data vs. compute vectorization. We analyze how transforming unstructured real-world entities into continu ous\, high-dimensional dense numeric representations provides critical top ological context for downstream similarity checks. Moving past standard ea ger-mode execution patterns\, we demonstrate how custom OpenAI Triton and CUDA fused memory kernels mathematically combine scale\, mask\, and softma x routines into localized execution blocks inside fast SRAM registers\, by passing HBM (High Bandwidth Memory) latency loops completely. Furthermore\ , this session explores advanced index topologies—specifically Hierarchi cal Navigable Small Worlds (HNSW) coupled with Vector Quantization (VQ) bo undaries. By applying Vector Quantization to compress vast floating-point vector spaces into low-bit indices\, we illustrate how to bridge the gap b etween continuous neural state spaces and discrete tool selection loops. F inally\, we synthesize these optimization mechanics within the framework o f Agentic AI control loops (ReAct- Reasoning and Acting)\, providing a uni fied approach to cognitive scaling\, multi-agent synchronization\, and rea l-world hallucination mitigation.\n\nSpeaker(s): Baris Kazar\, \n\nRoom: P L128B\, Bldg: School of engineering\, Technologyand Aeronautics\, 2500 Nor th River Rd\, Manchester\, New Hampshire\, United States\, 03106 LOCATION:Room: PL128B\, Bldg: School of engineering\, Technologyand Aeronau tics\, 2500 North River Rd\, Manchester\, New Hampshire\, United States\, 03106 ORGANIZER:bbancroft@ieee.org SEQUENCE:21 SUMMARY:Advanced Latent Representations and Computational Optimization: A S ummary of PyTorch Experiences in Deep Architectures\, RAG\, and Agentic AI : Vector Embeddings\, Vectorization and Vector Quantization URL;VALUE=URI:https://events.vtools.ieee.org/m/580581 X-ALT-DESC:Description: <br /><p>As deep learning models scale into multi-b illion parameter spaces\, modern enterprise architectures face an immediat e barrier: the memory bandwidth wall. Standard operator execution patterns bottleneck high-performance systems by forcing intermediate tensor states to repeatedly cycle through high-bandwidth memory (HBM). This lecture add resses the mathematical and hardware codesign patterns required to achieve sublinear search mandates and drive maximum runtime execution throughput across real-time\, multi-modal environments. Through a deep dive into PyTo rch implementation mechanics\, we examine the dual paradigms of data vs. c ompute vectorization. We analyze how transforming unstructured real-world entities into continuous\, high-dimensional dense numeric representations provides critical topological context for downstream similarity checks. Mo ving past standard eager-mode execution patterns\, we demonstrate how cust om OpenAI Triton and CUDA fused memory kernels mathematically combine scal e\, mask\, and softmax routines into localized execution blocks inside fas t SRAM registers\, bypassing HBM (High Bandwidth Memory) latency loops com pletely. Furthermore\, this session explores advanced index topologies&mda sh\;specifically Hierarchical Navigable Small Worlds (HNSW) coupled with V ector Quantization (VQ) boundaries. By applying Vector Quantization to com press vast floating-point vector spaces into low-bit indices\, we illustra te how to bridge the gap between continuous neural state spaces and discre te tool selection loops. Finally\, we synthesize these optimization mechan ics within the framework of Agentic AI control loops (ReAct- Reasoning and Acting)\, providing a unified approach to cognitive scaling\, multi-agent synchronization\, and real-world hallucination mitigation.</p> END:VEVENT END:VCALENDAR