Research & Insights — Post-Neumann Computing & AI Architecture

The future of AI hardware isn’t faster GPUs. It’s purpose-built silicon that thinks differently.

Every improvement in the GPU era has been incremental — more cores, more memory, more power. Punky Tiger Labs is built on the opposite premise: inference is a computing problem, not a graphics problem. We design the architecture first, then the transistor, then the compiler. The result is hardware that executes cognitive workloads deterministically, with bounded latency and persistent state.

Core pillars

Four research areas.

The technical surface that every PTL invention touches — from the transistor to the model runtime.

Architecture

Post-von-Neumann Computing

Unified cognitive tiles that fuse memory and compute on the same substrate. Eliminates the bus bottleneck that has defined processors since 1945.

Inference

Deterministic AI Inference

Bounded latency, predictable tail behavior, zero cache misses. Hardware-level scheduling turns AI inference into a real-time system.

Security

Hardware-Level Security

Attestation, steganographic watermarking, and adversarial-resistant encoding anchored in silicon — not bolted on as middleware.

Quantum-ready hybrid computing architecture

Forward

Quantum-Ready Architectures

Hybrid classical–quantum interfaces designed so today’s workloads port to tomorrow’s accelerators without rewriting the stack.

Publications

Upcoming research.

Four papers currently in preparation. Titles and abstracts are locked; full releases coming in 2026.

2026

Post-Neumann Architecture: A Unified Cognitive Substrate

Foundational paper introducing the tile-based cognitive computing model that replaces the CPU/memory split with fused compute-storage elements.
Coming 2026
2026

ZLTA-2: Zero-Latency Token Architecture for Transformer Inference

Predictive token dispatch, speculative pipelines, and hardware-accelerated attention scoring that push inference below the 0.1 ms threshold.
Coming 2026
2026

AI-SRAM Tiles: Compute-in-Memory at Transistor Density

A circuit-level study of AI-SRAM tiles — the self-contained compute-plus-storage element that serves as the Post-Neumann building block.
Coming 2026
2026

State Capsules: Hardware-Managed Persistent Inference

How silicon-level state management turns stateless transformer models into persistent, session-aware systems with near-zero resumption cost.
Coming 2026

Architecture briefings

Three insights. Open to read.

Short-form briefings from the PTL research team. Click a card to expand the full article.

The Von Neumann Wall

Architecture · 8 min read · March 2026

Since 1945, general-purpose compute has been defined by a single architectural choice: separate compute from memory, and move data across a bus between them. It worked. Then it stopped scaling.

The structural ceiling

Modern inference workloads are bandwidth-bound long before they are compute-bound. A transformer layer is not arithmetically expensive — it is expensive because every matrix multiply requires shuttling weights from DRAM to SRAM to registers and back. Each hop is an energy tax. Each hop is a latency tax. Each hop is the Von Neumann wall.

What tiles do differently

A tile is a self-contained cell with local SRAM, local arithmetic, and local control. Instead of streaming data to a global execution unit, the model is mapped onto the tile grid and the work flows where the weights already live. There is no bus between compute and storage because there is no separation.

Inference becomes a topology problem, not a bandwidth problem.

The downstream effect

Determinism emerges almost for free. If every weight has a known physical address on a known tile, then every attention head has a known cost. Worst-case latency stops being a statistical distribution and becomes a bounded constant. That is the property that lets inference join real-time systems.

Punky Tiger Labs Research — Architecture team, March 2026.

Why Determinism Matters

Inference · 6 min read · February 2026

Cloud inference dashboards love the median. Production systems run on the tail. The gap between the two is where deterministic hardware earns its keep.

The median is a lie

A GPU that averages 12 ms per token but spikes to 180 ms at the 99.9th percentile will fail any real-time contract. Robotics, autonomous systems, and interactive agents don’t care what you average — they care what you guarantee. Stochastic schedulers, cache evictions, and DRAM row conflicts are the sources of the spikes, and they are structural in the GPU model.

Bounded, not fast

Deterministic inference isn’t about being the fastest. It’s about being predictable. When every weight has a fixed tile, every attention head has a fixed schedule, and every memory access has a fixed cycle count, the worst case collapses onto the best case. Tail latency stops being a probability distribution and becomes a specification.

Predictable is the new fast.

What it unlocks

Real-time robotics. Sub-frame gaming inference. Closed-loop industrial control. Agentic systems with latency SLAs. These are the workloads the stochastic GPU model cannot serve — and the ones Post-Neumann silicon is purpose-built for.

Punky Tiger Labs Research — Inference team, February 2026.

Hardware-Rooted Authentication

Security · 7 min read · January 2026

Model weights are intellectual property. Model outputs are legal artifacts. If your authentication path runs through software, you are trusting the adversary’s environment.

The substrate is the proof

A hardware root of trust means the attestation device is the same device that runs the inference. There is no handoff, no intermediate driver, no OS kernel in the trust path. The model signs with a key that exists only inside the tile. The output is provable back to a specific piece of silicon at a specific instant.

Steganographic watermarking

Every token emitted by a Post-Neumann tile carries a hardware-embedded signature that survives downstream re-encoding, paraphrasing, and model-to-model distillation. The signal sits below the linguistic surface — invisible to the output, legible to the verifier.

The output knows which silicon produced it.

Why it matters now

Provenance is moving from a courtesy to a legal requirement. Regulators, content platforms, and enterprise buyers increasingly demand that AI-generated artifacts trace back to a specific, accountable source. Hardware-rooted authentication is the only layer that can deliver that guarantee without relying on the good behavior of the software stack.

Punky Tiger Labs Research — Security team, January 2026.

External validation

Independent research agrees.

Recent peer-reviewed and industry papers that converge on the same architectural conclusions we’ve been building toward.

Inference throughput arXiv · Feb 2026

FAST-Prefill: Decoupled Attention for Long-Context Inference

Decouples prefill from decode via a split memory hierarchy — the same design principle behind ZLTA-2’s predictive dispatch pipeline.

Independent validation of memory-tier separation for transformer inference.

Heterogeneous compute Zhao & Liu · Jan 2026

Heterogeneous AI Compute: A Survey of Tile-Based Accelerators

A survey of emerging tile-grid accelerators confirms the industry shift toward the fused compute-storage topology PTL patented years earlier.

Independent validation of the tile paradigm as the post-GPU direction.

KV-cache efficiency Zhang · Jan 2026

SwiftKV: Streaming KV-Cache Eviction for Long-Context Models

Demonstrates that state persistence dominates inference cost at long context — precisely the regime State Capsules are built for.

Independent validation of persistent-state hardware as the bottleneck.

Memory fabric Kim et al. · Nov 2025

CXL-Enabled KV-Cache: Towards Disaggregated Inference Memory

Early industry experiments with CXL-backed KV caches rediscover the need for a unified memory-compute substrate — the PTL thesis since day one.

Independent validation of unified memory-compute topology.

The technology page shows how these research pillars land in silicon — and the patents page shows how they’re protected.

We don’t optimize. We Rearchitect.

Four research areas.

Post-von-Neumann Computing

Deterministic AI Inference

Hardware-Level Security

Quantum-Ready Architectures

Upcoming research.

Post-Neumann Architecture: A Unified Cognitive Substrate

ZLTA-2: Zero-Latency Token Architecture for Transformer Inference

AI-SRAM Tiles: Compute-in-Memory at Transistor Density

State Capsules: Hardware-Managed Persistent Inference

Three insights. Open to read.

The Von Neumann Wall

Why Determinism Matters

Hardware-Rooted Authentication

The Von Neumann Wall

The structural ceiling

What tiles do differently

The downstream effect

Why Determinism Matters

The median is a lie

Bounded, not fast

What it unlocks

Hardware-Rooted Authentication

The substrate is the proof

Steganographic watermarking

Why it matters now

Independent research agrees.

FAST-Prefill: Decoupled Attention for Long-Context Inference

Heterogeneous AI Compute: A Survey of Tile-Based Accelerators

SwiftKV: Streaming KV-Cache Eviction for Long-Context Models

CXL-Enabled KV-Cache: Towards Disaggregated Inference Memory

See the architecture behind the research.