Specialized Accelerator Architectures
Strategic orchestration of workload-specific silicon: breaking the Von-Neumann bottleneck.
AI Speed: LPUs & WSE
Deployment of Groq (LPU) and Cerebras (WSE) for deterministic sub-second AI inference and massive training.
Dataflow Compute & ICA
Deployment of the Next Silicon (ICA) dataflow architecture on sovereign RISC-V infrastructure — not a legacy offload, but a native non-Von-Neumann architecture in its own right.
Future Tech: Photonics & Neuromorphic
Roadmaps for Q.ANT photonic fabrics and SpiNNaker2 neuromorphic systems for 100x efficiency gains.
Roadmap: Deterministic AI Speed
| Phase | Strategic Action | Outcome |
|---|---|---|
| 1. Latency Audit | Profiling TTFT (Time To First Token) on NVIDIA Blackwell clusters vs. Groq specs. | Benchmark baseline for ROI analysis. |
| 2. Memory Mapping | Optimizing LLM weights for Cerebras CS-3 SRAM. | Elimination of DRAM bottlenecks. |
| 3. Dataflow Deployment | Integration of SambaNova RDUs for RAG workloads. | Scalable enterprise inference. |
Dataflow & Sovereign Silicon
Next Silicon's Intelligent Compute Architecture (ICA) is a native dataflow architecture: computation is driven by data availability, not a sequential instruction stream. A grid of compute units passes results directly from unit to unit, eliminating the control overhead of conventional CPUs.
- Not an offload trick. A compute architecture in its own right — branch prediction and out-of-order logic disappear, freeing the die area they consume on conventional CPUs.
- Bring your own code. C++, Fortran and Python run unmodified — runtime software identifies hot paths and reconfigures the hardware automatically, with no domain-specific language.
- Sovereign by design. A RISC-V host (Arbel) plus a RISC-V-based accelerator — a full CPU-plus-accelerator stack free of ARM licensing and the associated export-control exposure.
- Proven in production. Already deployed in HPC systems including Sandia National Laboratories; vendor-reported figures of up to 10x performance over leading GPUs at materially lower power.
Where dataflow fits
Data-driven, highly parallel workloads where conventional processors spend most of their silicon on control logic rather than computation:
Roadmap: Sovereign Silicon & Dataflow
| Phase | Strategic Action | Outcome |
|---|---|---|
| 1. ISA Evaluation | RISC-V vector extension mapping for legacy x86 code; Arbel host-CPU assessment. | Migration risk report. |
| 2. Dataflow Mapping | Profiling application hot paths for ICA execution; onboarding unmodified C++/Fortran/Python. | Hotspot-to-dataflow fit report. |
| 3. Adaptive Deployment | Next Silicon Maverick integration with runtime reconfiguration; staged CPU fallback where ISV certification is pending. | Transparent, de-risked acceleration. |
| 4. Compliance Hardening | Root-of-trust audit for the open-source silicon architecture across host and accelerator. | Digital sovereignty & security. |
Roadmap: Post-Silicon Scaling
| Phase | Strategic Action | Outcome |
|---|---|---|
| 1. Photonics Audit | Profiling I/O latencies against Q.ANT optical fabric. | Identification of MAC bottlenecks. |
| 2. Neuromorphic Sync | Hybrid SpiNNaker2 edge-inference co-design. | 60% TCO reduction. |
| 3. Exascale Setup | Final PCIe/CXL integration into production HPC environments. | AI-native research environment. |
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