Kevin D. Johnson
HPC+AI Infrastructure · Neuromorphic Computing · Quantum-Centric Supercomputing · Forward-Deployed Engineering
Recent Focus Areas
Where infrastructure meets intelligence.
Palantir Foundry / AIPThree integrations — entity discovery + ontology enrichment, multi-modal targeting, and autonomous ontology generation via SymWisdom. |
LLM & Gen AI InfrastructurevLLM, GPFS KV cache sharing, semantic routing, Nemotron-120B reflection engine, multi-LLM orchestration at scale. |
Neuromorphic & Heterogeneous Compute10-chip AKD1000 hive mind, QPU+NPU+GPU+CPU orchestration, sub-millisecond edge inference across 12 silicon architectures. |
Autonomous Ontology DesignSystems that discover, name, and structure concepts from continuous perception — no human modeling required. |
Data Platform ArchitecturePetabyte-scale GPFS, mmap’d shared state across 11 nodes, experience lifecycle management, three-tier KV cache replacing Redis. |
Forward-Deployed Engineering13 years delivering to government, research, financial services, healthcare, and life sciences. |
Technical Papers
Peer-style research on heterogeneous compute orchestration, neuromorphic systems, and quantum-classical integration.
Symphony as Compute Ontology: Extending Insight into OpenShift and NVIDIA AI Factories
April 2026
Presents IBM Spectrum Symphony as the compute ontology for OpenShift and NVIDIA AI factory infrastructure, formalizing the categorical distinction between container placement engines and compute ontologies. ELIM delivers typed resource metrics with semantic direction, consumer hierarchies provide unlimited-depth organizational governance with sub-second rebalancing, SOAM manages service lifecycles with per-phase failure policies, and cross-substrate routing spans OpenShift clusters, bare-metal GPU hosts, cloud burst instances, and heterogeneous accelerators under one workload management domain. A detailed comparison with Kueue (v0.17) demonstrates that Kubernetes’ strongest governance extension operates at alpha API maturity with zero capability in five of six ontological dimensions. A feature-complete analysis of Run:ai (v2.24) establishes that every Run:ai capability is replicable within Symphony’s ELIM architecture using exclusively public GPU APIs. Three engagement modes — Ontology Enrichment, Ontology Governance, and Ontology Subsumption — accommodate extending the compute ontology into OpenShift without modifying the container platform. Part I of two; a companion paper will present empirical validation across three compute substrates.
Solving the One and the Many with LSF, Symphony, GPFS, and RHEL AI: A Dynamic Compute Platform for NVIDIA AI Factories
March 2026
Presents a multi-ontology architecture for AI factory workload management where IBM Spectrum LSF manages batch training, IBM Spectrum Symphony manages service-oriented inference, and IBM Storage Scale (GPFS) serves as the unified coordination substrate connecting the two compute domains. RHEL AI and vLLM provide the model serving runtime across NVIDIA, AMD, and Intel accelerators. Five demonstrations on commodity hardware validate the architecture, including multi-model vLLM inference, neuromorphic routing at 622 microseconds, cross-model KV cache transfer with 8.2x latency improvement, 47-second model handoff, and an Obfuscation-as-a-Service pipeline. Complements the earlier Sovereign AI OS paper, together spanning from neuromorphic workloads to large-scale GPU training and inference into Foundry.
Extending the Sovereign AI OS: Symphony as Compute Ontology for Palantir Foundry and NVIDIA
March 2026
Extends the Palantir-NVIDIA Sovereign AI OS Reference Architecture with IBM Spectrum Symphony as a heterogeneous compute orchestrator, enabling neuromorphic processors, quantum resources, edge sensors, and mainframe systems to participate as peer compute tiers alongside GPUs within Foundry’s governed ontology framework. Four working demonstrations validate the extension across autonomous ontology construction, cross-modal neuromorphic fusion, multi-paradigm trust verification, and AI-enabled financial ontology discovery.
High Performance Quantum-Centric Supercomputing: A Working Implementation of Heterogeneous Orchestration across QPU, NPU, GPU, CPU, and Other Tiers
March 2026
A reference architecture demonstrating that traditional batch schedulers require a fundamentally different approach for heterogeneous quantum-classical resources. Extends IBM Spectrum Symphony with proof-of-concept evidence across fifteen demonstrations spanning twelve silicon architectures, six compute tiers, and two network fabrics — orchestrating QPU, NPU, GPU, CPU, and mainframe as peer resource types under a single scheduling domain.
Commentary
Articles on AI infrastructure, neuromorphic computing, and the architecture of intelligence.
Fiction
Short stories — usually about bourbon, silicon, or both.
Technical Demonstrations
33 demonstrations built between December 2025 and May 2026.
| Date | Demonstration | Platforms |
|---|---|---|
| May 4 | SymSEAL: Neuromorphic Hive Mind for Special Operators — Ten special operators share a single neuromorphic perception layer, including drone overwatch, with twelve real BrainChip AKD1000 chips per helmet and 120 across the team under a 30-watt envelope per helmet. Symphony orchestrates the 120 inference units as one perceptive layer; GPFS serves as the cognitive substrate, providing distributed consensus across operator nodes. The concept parallels Anduril Industries’ EagleEye, however the AKD1000 chips and Symphony/GPFS together deliver an actual neuromorphic substrate sufficient to constitute a real hive mind, scaling identically from ten helmets and 120 chips to a hundred helmets and one thousand chips. The AKD1000 fires spikes at milliwatts; the hive identifies threats in microseconds to milliseconds; operator reaction remains bounded by motor neuron conduction velocity at 50 to 100 m/s and the seconds-scale pull from cortex through aim, intent, and trigger. The next-generation play would be closed-loop auto-fire driven by the hive itself, with the operator’s intent still up front and Rules of Engagement still binding the engagement; the hive accelerates the loop and does not replace the operator. The architecture is built and running today, traversing real BrainChip silicon alongside a simulated chip fabric, all routing through a combat simulation. A second demo will follow showing Anduril Industries’ Lattice and Palantir Technologies’ Foundry wired in for the engagement, including two tactical units operating concurrently with mission archetypes recognized across both engagements at once. ▶ video | NPU (AKD1000 x12/helmet, 120 total + simulated fabric), Intel N100, AMD EPYC, Symphony, GPFS, Palantir Foundry, Anduril Lattice, ARMA 3 |
| Apr 25 | FireMesh: Neuromorphic Wildfire Detection across Public Satellite Data — Palisades Fire replay shows the pipeline classifying every hot pixel from GOES-18, VIIRS, MODIS, and GLM as wildfire or confounder in roughly three milliseconds on simulated BrainChip Akida V2 chips with no GPU and no cloud round-trip. Symphony orchestrates the ingest layer: services pull FIRMS every 60 seconds, decode GOES FDCA every 5 minutes, and fetch L1b radiance via S3 byte-range so a 5 KB slice replaces a 160 MB scan. Decoded products land on GPFS shared storage, mounted across every node so the same bytes serve historical playback and live inference together. Detections fan into a 36-node Akida fleet: three nodes serve a tabular FIRMS classifier, thirty-three run a spatial L1b model trained on six historical California fires and gas-flare negatives from the Permian, Bakken, and Eagle Ford. 3.5 ms per classification, 8 KB of weights, edge-deployable without an NVIDIA card. The harder story is not speed but discrimination: wildfire versus refinery flare versus ag burn versus metal-roof reflection, the difference that makes the alert worth sending. AKD1500 silicon path could project the same orchestration onto a verdict stream that ICS section chiefs, county OES, and mutual-aid dispatch could subscribe to with geo-fenced AOR, confidence, and latency surfaced inline. ▶ video | NPU (Akida V2 SDK x36 sim), Symphony, GPFS, AWS S3 (GOES-18, VIIRS, MODIS, GLM), NASA FIRMS, NASA Earthdata/LAADS (VIIRS VNP14IMG), Copernicus Data Space STAC (Sentinel-1/2/3, Landsat), USGS EROS M2M, Cal Fire DINS, LA County assessor parcels, MTBS burn perimeters/dNBR, Mapillary street-view, PurpleAir, AirNow, OpenSky ADS-B, Wingbits, AISStream |
| Apr 19 | Shattered Crown in Anduril Lattice: Full-Chain C2 Integration — The Ardenath: Codex of Portents rendering migrated from the original parchment interface into Anduril Industries Lattice, driven by live BrainChip Akida v2 classifications from SymWisdom’s Symphony cluster and the same self-assembling ontology in Palantir Foundry. The full chain now runs Lattice, Akida, Symphony, NVIDIA LLM, Foundry, and GPFS as a single integrated pipeline. Integration completed in about three hours, bridging data flow between platforms. Lattice does not render fantasy maps because it is a production C2 platform involved in real military work; 31 Banner Lords and the Waystones monitoring them were positioned in open Pacific waters, transformed from the original Stable Diffusion parchment with its auto-drawn borders, and redrawn in fine tactical fashion in Lattice. 36 nodes with 36 Akida 1500 simulations run real-time, while continuous neuromorphic perception classifies a synthetic theater and drives the map’s function. The operator sees both activity and coordination with full provenance attribution back to Foundry. The architecture enables policy-led command and control alongside neuromorphic experience and collected wisdom crystallization as the system continues to operate. ▸ screenshot | Anduril Lattice, Akida V2 SDK (TENNs-PLEIADES) x36 sim, KVM fleet, Symphony, GPFS, Nemotron Cascade 30B, Palantir Foundry |
| Apr 18 | SymHeart: Vital-Signs Biometrics on Symphony Community Edition — Seeed Studio MR60BHA2 60 GHz mmWave Human Breathing and Heartbeat Sensor streaming into a simulated BrainChip AKD1500 through Symphony Community Edition, a four-node Docker Symphony cluster running on a laptop. Live dashboard shows heart rate (64 bpm), respiration (24/min), target distance (57 cm), a 5-second heart-phase waveform, and AKD1500 class logits from the remote AkidaGenericService. Model pipeline: InputData(32,1,1) → Conv(5×1,8)+MaxPool(2,1) → Conv(3×1,16) → FC(4), mapped to the AKD1500 virtual device at ~96µs inference. Live demonstration at the BrainChip booth, Microelectronics US 2026, April 22 and 23. ▸ screenshot | NPU (AKD1500 sim), mmWave sensor (Seeed MR60BHA2), Symphony Community Edition, Docker |
| Apr 16 | Ardenath: Codex of Portents — Neuromorphic Loom on a KVM Fleet — Kingdom of the Shattered Crown: a fantasy frame for a 36-chip simulated Akida 1500 fleet running TENNs-PLEIADES across 36 KVM instances. 31 Waystones and 5 sworn watch regional Banner Lords across an ink-drawn continent; a council of Augurs keeps vigil. Each simulated chip listens for nine distinct behavioral signatures in its assigned region, feeling neuron spike patterns fire. Symphony orchestrates every node; GPFS binds the shared observational substrate so no two chips see different versions of the truth. Nemotron Cascade (30B) reflects on edge activity and Banner Lord coordination; an ontology crystallizes itself into Palantir Foundry without manual definition — the same self-assembling pattern used by SymWisdom and SymRail. The identical architecture already runs against 10 real AKD1000 chips watching American freight rail economics at ~1 mW per inference, whiskey distillery fermentation on a rotating partition, and OSINT across ten aggregated modalities. ▶ video ▸ full writeup | Akida V2 SDK (TENNs-PLEIADES) x36 sim, KVM fleet, Symphony, GPFS, Nemotron Cascade 30B, Palantir Foundry |
| Apr 11 | Akida V2 SDK Capacity Demo: Symphony Across On-Prem + IBM Cloud — Capacity and scaling demo of the BrainChip Akida 1500 V2 SDK across three independent Symphony clusters: an on-prem EPYC Rome VM fleet, a 10-node IBM Cloud Symphony in Washington DC (Cascade Lake bx2-4×16), and a 36-node IBM Cloud Symphony in Dallas. TENNs-PLEIADES spatiotemporal model. Per-physical-core throughput converged at ~90 inf/sec ±5% across all baselines; per-context RAM 40.6–40.8 MB (within 0.3%); cross-cluster scaling 99.4% of ideal. The combined 46-node IBM Cloud fleet held 8,832 concurrent V2 inference contexts in ~349 GB aggregate RAM. Demonstrates Symphony as a horizontal scaling substrate for neuromorphic V2 simulation: hardware-deterministic and linearly scalable across cloud regions. AKD1500 silicon path projected at 32k–160k inf/sec per chassis (32 chips, ~10 W) — one silicon chassis ≈ 4–19× the 46-node cloud sim fleet at ~1% of the power. | Akida V2 SDK (TENNs-PLEIADES), EPYC Rome, Cascade Lake (bx2-4×16), Symphony, IBM Cloud (WashDC + Dallas) |
| Apr 9 | SymRail: Neuromorphic Freight Intelligence — 10 live Railfan cameras across the United States feed frames to 10 AKD1000 chips, each assigned a camera location from Rochelle, Illinois to Folkston, Georgia. 9-class railcar classifier (tank cars, intermodal containers, grain hoppers, coal hoppers, autoracks, boxcars, empty flats, locomotives, no train) trained via transfer learning from ImageNet backbone. V1 hit 85.6% on hardware; V2 reached 91% after augmenting with 4,247 auto-labeled crops from a YOLOv8n detector, quantized to 4-bit weights and activations fitting in 1 MB SRAM. Spike records flow through shared memory into GPFS archives. Tank car counts at strategic junctions are a leading indicator for petroleum logistics — comparable freight visibility to satellite data services costing >$50k/yr, generated from public cameras on neuromorphic chips. Flask dashboard with live US map. ▶ video | NPU (AKD1000 x10), NVIDIA GPU (YOLOv8n), Symphony, GPFS, Palantir Foundry |
| Apr 3 | Cognitive Integrity Standard: Neural Media Scoring — Quantitative framework measuring what media content does to the brain during processing. Meta/FAIR TRIBE v2 multimodal brain encoding model predicts cortical activation across 20,484 vertices; 10 AKD1000 chips run custom spiking neural nets for real-time EEG signal processing trained on clinical EEG datasets (Temple University Hospital, CHB-MIT, Siena, PhysioNet, DTU). Five neural dimensions — Analytical Engagement, Emotional Activation, Self-Referential Insertion, Memory Encoding Intensity, Social Cognition Activation — combine into a Manipulation Index where every term includes (1 – Analytical Engagement): content that engages critical thinking mathematically cannot score as manipulative. Scored eight outlets’ coverage of the Supreme Court’s 6-3 tariff decision. CNBC and Fox Business scored highest across every dimension — markets are personal, money is emotional, and financial reporting engages the prefrontal cortex. Opinion-heavy cable (MSNBC, CNN) scored lowest on analytical and emotional activation alike, suggesting low overall cortical engagement rather than manipulation. Normalization calibrated against a 75-stimulus corpus spanning six content categories. ▸ results | NPU (AKD1000 x10), NVIDIA GPU (TRIBE v2), Symphony, GPFS |
| Apr 2 | SymIntercept: Autonomous Missile Defense — Full kill chain in under 200ms: sensor fusion, threat classification, trajectory prediction, interceptor dispatch. Counters hypersonic glide vehicles and saturation attacks at Mach 7+. Rules of Engagement defined as Foundry ontology objects — queryable, auditable, version-controlled policy propagated to the edge. Cryptographic provenance via CKKS homomorphic encryption with QRNG-seeded Shamir secret sharing for N-of-M independent sensor confirmation. 10 AKD1000 chips classifying IR blooms, radar returns, and EW signatures in under 1ms at milliwatts. Two scenarios: single-radar refusal (provenance blocks engagement) and multi-sensor confirmed intercept from four defense assets. TTS narration via Qwen3-TTS. ▶ video | NPU (AKD1000 x10), NVIDIA GPU (Qwen3-TTS), Symphony, GPFS, Palantir Foundry, OpenFHE |
| Mar 27 | SymWisdom WorldMonitor: Akida Intelligence Panel — 10 geopolitical OSINT domains (maritime AIS, aviation, news, markets, infrastructure, seismic, cyber, Telegram, energy, displacement) classified by 10 AKD1000 chips in sub-microsecond inference at ~10μJ. Three-domain model swap in 31ms via GPFS scheduler. ELIM-driven autonomic scheduling: critical domain count and focus mode as Symphony resource metrics trigger GPU scaling, premium feed acquisition, and crisis posture shifts. Built in one day. ▸ screenshot | NPU (AKD1000 x10), NVIDIA GPU (vLLM), Symphony, GPFS, Palantir Foundry |
| Mar 25 | SymPalantir RAG: Trust Architecture for RAG Poisoning — Four-layer trust pipeline for RAG ingestion: cryptographic signatures, multi-party approval, Nemotron-120B semantic review, and neuromorphic anomaly detection on 10 AKD1000 chips (9ms). Embeddings encrypted end-to-end via CKKS homomorphic encryption. Full attack-then-defense scenario with TTS narration. Built in three days. ▶ video ▸ full writeup | NPU (AKD1000 x10), NVIDIA GPU (Nemotron-120B), Symphony, GPFS, Palantir Foundry, IBM Quantum |
| Mar 24 | NeuroDOOM 50: 50 Simultaneous DOOM Instances on 10 AKD1000 Chips — 50 VizDoom instances on the same 10 chips; All orchestrated by IBM Spectrum Symphony with Ozzy Osbourne crossover audio from the hive mind music demo. The hive mind scales. ▶ video | NPU (AKD1000 x10), Intel N100, Symphony, GPFS |
| Mar 23 | SymWisdom Part II: Encrypted Multi-Domain Cognition — Homomorphic encryption at consciousness boundary via OpenFHE/IBM Quantum, multi-domain neuromorphic swap (defense + whiskey distillery) across 15 models, 6-phase self-introspection framework, 3,845 reflections, 13 wisdom objects including first cross-domain object ▸ full writeup | NPU (AKD1000 x10), NVIDIA GPU (vLLM), Symphony, GPFS, Palantir Foundry, IBM Quantum, OpenFHE |
| Mar 20 | SymWisdom: The Experiencing LLM — 10 AKD1000 chips perceiving across 7 modalities, Nemotron-120B reflecting every 45 seconds, 6 wisdom objects crystallized into Foundry from 1,646 reflections in 40 hours ▸ full writeup | NPU (AKD1000 x10), NVIDIA GPU (vLLM), Symphony, GPFS, Palantir Foundry |
| Mar 16 | NeuroDOOM: Hive Mind Learns to Play DOOM — 10 AKD1000 chips as hive mind playing DOOM; ~4ms per-chip inference, 35fps real-time at ~10W total. Plays DOOM so fast it’s hard to watch. ▶ video | NPU (AKD1000 x10), Intel N100, Symphony, GPFS |
| Mar 11 | Neuromorphic Hive Mind Music — 10 AKD1000 chips as ensemble, each trained on distinct catalog slices; played Pachelbel’s Canon in D and Ozzy Osbourne’s Crazy Train ▶ Canon ▶ Crazy Train | NPU (AKD1000 x10), Intel N100, Symphony, GPFS |
| Mar 4 | Non-Extractive Targeting with Symphony, Akida, and Foundry — 10-chip multi-modal sensor fusion across 7 modalities with Symphony emergence engine writing confirmed events to Foundry; built in five days ▶ video ▸ full writeup | NPU (AKD1000 x10), Symphony, GPFS, Palantir Foundry |
| Feb 20 | Neuromorphic Deepfake Voice MFA — Three-layer auth: KeyCloak OIDC/JWT, Akida voice biometric (109μs, 92.6%), adversarial training vs Qwen3-TTS ▶ video | NPU (AKD1000), NVIDIA GPU (Qwen3-TTS), Symphony, GPFS |
| Feb 18 | vLLM + GPFS KV Cache Sharing — 16 llm-d algorithms + 15 capabilities reimplemented on Symphony; three-tier GPFS cache replacing Redis; cross-model KV transfer Granite 2B to 8B to 34B | NVIDIA GPU (vLLM), Symphony, IBM Cloud, GPFS (three-tier KV cache w/ ILM) |
| Feb 10 | Akida Behavioral Biometrics — Voice emotion analysis of Palantir earnings livestream; 71μs inference, 89.3% confidence; detected vocal excitement 2-3 seconds before 15x volume spike ▶ video | NPU (AKD1000), Symphony, GPFS, Polygon.io |
| Feb 7 | Quantum-Neuromorphic Portfolio Pipeline — Four-tier workflow: PQFM quantum feature encoding (16-qubit Heisenberg), Akida regime classification, LLM risk narratives, z/OS COBOL settlement ▸ architecture | QPU (Heron R3), NPU (AKD1000), NVIDIA GPU (vLLM), z/OS, Symphony, IBM Cloud, GPFS |
| Feb 3 | Akida Market Regime Classifier — Real-time market regime classification: 93.47% accuracy, 622μs latency, 30mW on AKD1000 | NPU (AKD1000), Intel N100, Symphony, GPFS |
| Jan 20 | Symphony Mainframe: Conversational z/OS — Natural language interface for COBOL actuarial on Wazi aaS | z/OS (Wazi aaS), Symphony, IBM Cloud |
| Jan 17 | Neuromorphic Symphony: GPU HBM as Storage Tier — Spike-driven data lifecycle with GPFS + DMAPI + Norse LIF neurons | NVIDIA GPU, Symphony, GPFS |
| Jan 12 | Quantum-Classical Integration Suite — Four Qiskit applications on GPU simulation and IBM Quantum Heron R3; 2,560 hyperparameter combinations | QPU (Heron R3), NVIDIA GPU (6x A100), Qiskit, Symphony, IBM Cloud, GPFS |
| Jan 12 | KNN Semantic Router iPhone App Video Demo — Live iPhone/Android app; queries classified and routed across Granite model tiers with ELIM metrics ▶ video | NVIDIA GPU (vLLM), Symphony, GPFS |
| Jan 7 | PowerVS + Symphony Actuarial — Natural language interface for COBOL actuarial programs on IBM PowerVS; no COBOL rewrite required | IBM Power (PowerVS), Symphony, IBM Cloud |
| Jan 6 | Semantic Router / LLM Query Routing — KNN semantic classification routing queries to Granite model tiers for 30-50% cost reduction | NVIDIA GPU (vLLM), Symphony, GPFS |
| Dec 29 | Palantir + Symphony: Cognitive Infrastructure — Ontology Intelligence discovering 1,804 entities and 2,690 relationships from financial services data | Symphony, GPFS, Palantir Foundry, Granite LLM |
About
I work at the intersection of high-performance computing, AI infrastructure, and the data platforms that hold them together. Most of my career has been spent building and deploying these systems for organizations where downtime or bad answers aren’t an option. That includes financial services, government, defense, healthcare, and research.
I’m a Field CTO at IBM, focused on the HPC Cloud portfolio, which includes Spectrum Symphony, Storage Scale, and LSF. Before that, I spent over a decade in consulting engagements across the same stack. That work covered petabyte-scale parallel file systems, real-time workload orchestration, and the infrastructure underneath large-scale AI. Before IBM, I managed HPC biocomputing infrastructure at TGen, ran a specialty coffee company, and worked in storage virtualization and disaster recovery.
Lately, most of my independent work has been exploring what happens when neuromorphic hardware, large language models, and distributed orchestration frameworks are combined in ways their designers didn’t anticipate. The articles on this site come from that exploration.
I hold an MBA and a Master of Accounting in Finance from Keller Graduate School, an M.A. in Theology from Fuller Theological Seminary, and an M.S. in Global Technology and Development from Arizona State University, where I’m currently a Ph.D. candidate in Innovation in Global Development. My research examines how coordination infrastructure shapes what we can know, decide, and imagine, using computational AI and simulation methods to compare architectures at scale, arguing that infrastructure building is itself a mode of development theorizing.