Deep Learning Kernels — Accelerator Optimization
Jan 1, 2021
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1 min read
Project Details
01
Accelerator Framework
Developed a PyTorch and TensorFlow software suite for high-performance accelerator training and inference.
02
Kernel Debugging
Analyzed and resolved kernel bugs and performance bottlenecks.
03
TPC Mapping
Mapped PyTorch kernels to TPC kernels in C++.
04
Custom Operators
Developed operators using combinations of TPC kernels.
05
Model Export
Converted deep learning models to ONNX for deployment.
06
Graph Validation
Used Netron to visualize and validate exported model graphs.
07
Performance Focus
Optimized execution across accelerator training and inference workloads.
Tech Stack
Frameworks & Data
Python
NumPy
Pandas
PyTorch
TensorFlow
Acceleration & Tooling
C++
TPC Kernels
ONNX
Netron
VS Code
Linux
Git
JIRA
Authors
Lead Engineer — GenAI / Agentic AI & Applied ML
Lead Engineer with 5 years of hands-on experience developing and deploying GenAI, RAG, Agentic AI, and deep learning systems from scratch. I build production-grade AI platforms for realtime voice interaction, enterprise knowledge retrieval, regulatory search, and automated finance analysis. My work spans NLP, autonomous driving, computer vision, and Camera-LiDAR-Radar fusion, with a focus on reliable architecture, model optimization, and scalable data pipelines.
Stack: Python, GCP, GenAI, LLMs, Agents, Docker, FastAPI, Cloud SQL, Google ADK, Vertex AI, Agent Engine, Cloud Run, OpenAI Realtime API, WebSockets, Redis, MongoDB, Milvus, JWT, Docling, BM25, ModernBERT, vLLM, and FAISS.