One place for all technical research, industry insights, and thought leadership. Deep dives, LinkedIn articles, and industry frameworks โ everything searchable and organized.
โญ Research Highlights
The newest and most impactful research from CSยฒB Technologies
Complete curriculum for understanding LLMs from two perspectives. Architecture Track: Data preparation, training loops, optimization techniques, inference optimization, deployment pipelines.
Implementation Track: FSDP internals, parallelism strategies, tensor cores, memory hierarchy. Why is AllReduce taking 40% of your step time? What's actually happening inside a tensor core?
Comprehensive technical analysis of the AI accelerator landscape covering GPU, TPU, and custom ASIC architectures from NVIDIA, AMD, Intel, Google, AWS, and emerging players.
Comprehensive research into BlueField DPU performance under AI microburst workloads. NVIDIA ASTRA architecture, E/W latency degradation, and real-time QoS enforcement.
Comprehensive market analysis covering frontier models, protocol standardization (MCP/A2A), production frameworks, OWASP Agentic Security Top 10, and enterprise deployment strategies.
๐ก LinkedIn Insights
Industry analysis and commentary published on LinkedIn
Complete curriculum for understanding LLMs from two perspectives: Architecture Track (tensor cores, memory hierarchy, interconnects) and Implementation Track (FSDP, parallelism strategies, ZeRO, quantization).
Deep dive into GPU execution of large language models. From token embedding to output generation โ understanding the complete inference pipeline and what happens at each stage on the hardware.
Technical breakdown of why Blackwell dominates tensor operations. Architecture differences, memory bandwidth, tensor core evolution, and the software ecosystem advantage.
A deep dive into what happens when you train a Large Language Model. Tracing the complete path from high-level Python code through the compiler stack down to GPU silicon execution.
Continuation of the deep dive into LLM training. Further exploration of the compilation stack, optimization passes, and how your model actually executes on GPU hardware.
Large-scale AI training is fundamentally bottlenecked by fault tolerance. Current checkpointing stalls GPU compute for seconds to minutes. The solution? Intercept state at the UCIe die-to-die interconnect.
How CXL memory pooling and Ultra Ethernet Consortium standards combine to enable disaggregated, composable AI infrastructure at scale.
๐ง Cognitive Neuroscience
Bridging biological cognition and artificial intelligence
Exploring the parallels between human working memory and transformer context windows. How biological constraints inspire AI design, and what neuroscience teaches us about attention mechanisms.
What if we trained AI the way children learn language? Exploring curriculum learning, developmental stages, and how cognitive science principles could revolutionize LLM training methodologies.
๐ค Agentic AI
Multi-agent systems, communication protocols, and orchestration frameworks
Comprehensive analysis of agentic AI systems including frontier models, communication protocols (MCP, A2A, ACP), orchestration frameworks, OWASP security guidelines, and memory architectures.
Exploring the next generation of AI interface protocols that enable seamless agent-to-user and agent-to-agent interactions, reshaping how humans and AI systems communicate.
๐ AI Accelerators
GPU, TPU, and custom ASIC architectures for AI/ML workloads
GPU computing architecture from PTX binaries to kernel execution. CUDA compilation pipeline, architecture evolution from Pascal through Hopper to Blackwell and Rubin.
Explore CUDAROCm platform, HIP programming, AMDGPU compiler, and Instinct accelerators from MI250 to MI350X architecture.
Explore ROCmXLA compiler infrastructure, JAX programming framework, and TPU evolution from v1 through Trillium to Ironwood v7.
Explore TPUTrainium AI accelerators, Neuron SDK compiler infrastructure, NKI kernel programming, and evolution from Trainium 1 to 3.
Explore TrainiumDeep dive into GPU computing architecture, CUDA memory hierarchy, kernel execution models, and optimization techniques for high-performance computing workloads.
Read FundamentalsHead-to-head comparison of NVIDIA Tensor Cores vs AMD Matrix Cores (MFMA). Architecture differences, instruction sets, memory paths, and performance characteristics.
Read the inside story on the fundamental architectural decisions that led NVIDIA and AMD down divergent paths in tensor core design and memory hierarchy optimization.
๐ Networking
RDMA, Ultra Ethernet, and data center fabric architectures
Comprehensive analysis of high-performance networking for AI/HPC and storage. RDMA fundamentals, UEC architecture, memory operations, flow control, NVMe over Fabrics, AI collectives.
Deep dive into NVIDIA's Astra policy enforcement architecture and BlueField DPU performance characteristics under demanding AI workloads. Real-world benchmarks and optimization strategies.
Comprehensive guide to implementing ultra-low latency, hardware-enforced tenant isolation in AI infrastructure using DPU technology, NVIDIA ASTRA architecture, BlueField deep dives, and DOCA SDK programming.
๐พ Storage
GPU-storage integration, KV-cache optimization, and memory architectures
Publication-quality documentation on GPU-storage integration challenges. NVMe queue architecture, doorbell serialization, GPUDirect Storage, CXL memory semantics.
Read Full DocumentationMemory-efficient LLM serving using CXL-based intelligent memory endpoints. Per-head tracking, EMA-based attention scoring, RoPE-aware prefetch.
Read Full Referenceโ๏ธ Compilers
MLIR toolchains, federated learning, and distributed ML infrastructure
Comprehensive compiler supporting LLVM native code, WebAssembly binary encoding, Python transpilation, and direct interpretation with advanced type systems.
Read Full ResearchFault-tolerant parameter server architecture using Raft consensus for distributed ML, achieving 40%+ throughput improvement in federated learning scenarios.
View Full Documentationโ Frameworks
Standards alignment, challenge mappings, and solution frameworks