{AI Talks with Tea&Coffee #53 @ RIT} : Advanced Latent Representations and Computational Optimization: A Summary of PyTorch Experiences in Deep Architectures, RAG, and Agentic AI: Vector Embeddings, Vectorization and Vector Quantization
Abstract: As deep learning models scale into multi-billion parameter spaces, modern enterprise architectures face an immediate barrier: the memory bandwidth wall. Standard operator execution patterns bot- tleneck high-performance systems by forcing intermediate tensor states to repeatedly cycle through high-bandwidth memory (HBM). This lecture addresses 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 PyTorch implementation mechanics, we examine the dual paradigms of data vs. compute vectorization. We analyze how transforming unstructured real-world entities into continuous, high-dimensional dense numeric representations provides critical topological con- text for downstream similarity checks. Moving past standard eager-mode execution patterns, we demonstrate how custom OpenAI Triton and CUDA fused memory kernels mathematically com- bine scale, mask, and softmax routines into localized execution blocks inside fast SRAM registers, bypassing HBM (High Bandwidth Memory) latency loops completely. Furthermore, this session explores advanced index topologies|speci cally Hierarchical Navi- gable Small Worlds (HNSW) coupled with Vector Quantization (VQ) boundaries. By applying Vector Quantization to compress vast oating-point vector spaces into low-bit indices, we illustrate how to bridge the gap between continuous neural state spaces and discrete tool selection loops. Finally, we synthesize these optimization mechanics within the framework of Agentic AI control loops (ReAct- Reasoning and Acting), providing a uni ed approach to cognitive scaling, multi-agent synchronization, and real-world hallucination mitigation https://events.vtools.ieee.org/m/575616 Speaker(s): Dr. Baris Kazar, Rochester, New York, United States, Virtual: https://events.vtools.ieee.org/m/580546