# SSCCS × AWS Strategic Collaboration Roadmap

Self-evolving Autonomous Research & Validating System on AWS Infrastructure

Author

Affiliation

SSCCS Foundation [](mailto:contact@ssccs.org)

[SSCCS Foundation](https://ssccs.org)

Published

June 4, 2026

Abstract

This document outlines a proposed collaboration between the SSCCS Foundation and AWS to validate SSCCS on Graviton, HPC, and FPGA infrastructure, focusing on AI-driven compiler optimization and pre-silicon hardware emulation. We believe AWS is uniquely positioned to serve as the primary cloud platform for research into next-generation verifiable computing—no other provider offers the heterogeneous resource portfolio (ARM CPU, HPC cluster, FPGA fabric) that SSCCS validation requires. The document will be updated in real time, as the collaboration develops.

Other Formats

[LLMs](https://docs.ssccs.org/partnerships/aws.llms.md)

## Executive Summary

The SSCCS Foundation is advancing a novel computational paradigm that addresses the von Neumann “data movement wall” through structural observation rather than sequential instruction execution. To accelerate the research, validation, and deployment of this architecture, we are also developing [SSCCS neXus](../projects/nexus/): SSCCS’s proprietary knowledge infrastructure and autonomous research engine. neXus integrates an engine-agnostic GraphRAG pipeline with a closed-loop agentic workflow (Hypothesis & Research Planning → Execution → Verification → Generation) and contract-governed artifact generation, transforming theoretical compiler and hardware research into reproducible, auditable, and continuously optimized outputs.

**Important:** SSCCS is a pure research-stage project. No PoC is complete—all components are under active research and construction. What we have: a published formal model (whitepaper), Rust scaffolding with CI-verified RISC-V assembly, and the neXus autonomous research infrastructure.

neXus and SSCCS are inextricably linked. neXus is the autonomous engine that researches and validates SSCCS as a new computing model; SSCCS is the execution environment that structurally proves the knowledge neXus generates. Supporting both simultaneously requires a boundaryless research infrastructure where autonomous agents continuously hypothesize, compile, simulate, and verify across heterogeneous compute resources.

Specifically, we request AWS Activate credits and technical collaboration to execute three workstreams:

1.  AI‑Driven Autonomous Compiler Optimization on Graviton: Deploying agentic optimization systems on AWS Graviton instances to autonomously tune SSCCS compiler passes using cost models and liveness analysis.
2.  Hardware Emulation at Scale on AWS HPC: Utilizing AWS HPC clusters to simulate SSCCS behavior on future PIM/CIM architectures, and to evaluate energy efficiency and determinism before dedicated silicon becomes available.
3.  FPGA-Based Pre-Silicon Validation on AWS F1: Translating our verified RISC‑V assembly (golden reference model) to Verilog RTL, then synthesizing and validating on AWS F1 FPGA instances as the final hardware verification step before potential tape-out.

![](aws_files/figure-html/fig-partnership-overview-output-1.svg)

Figure 1: SSCCS × AWS collaboration: four interconnected pillars supporting joint research

## SSCCS: The Computational Shift

### The Von Neumann Bottleneck

For decades, mainstream computing has relied on the von Neumann model: sequential instruction execution, mutable state, and constant data movement between memory and processor. This architecture now faces well‑known barriers:

| Challenge | Impact |
|----|----|
| Data Movement Wall | 60–80% of energy spent moving data, not computing on it |
| Limited Concurrency | Explicit synchronization constrains parallel scaling |
| Lack of Verifiability | Black-box execution prevents auditable computation trails |

### SSCCS: Observation, Not Execution

SSCCS redefines computation as the deterministic observation of immutable structures:

![](aws_files/figure-html/fig-ssccs-concept-output-1.svg)

Figure 2: SSCCS ontological model: Field governs observation of Scheme and Segments

#### Formal Definition

The SSCCS observation model is expressed as:

\\P = \Omega(\Sigma, F)\\

where:

- \\\Sigma\\: Immutable Scheme (Segment set and structural relationships)
- \\F\\: Mutable Field (dynamic constraints)
- \\\Omega\\: Observation operator (computation event)
- \\P\\: Resulting Projection (computational output)

This formulation provides deterministic reproducibility for a given \\\Sigma\\ and \\F\\, which is important for verification in safety‑critical settings.

#### Energy Model

A simplified energy model for SSCCS:

\\E\_{total} = E\_{observation} \times N\_{obs} + E\_{field-update} \times N\_{update}\\

## Why AWS: A Unique Validation Platform

SSCCS requires significant and heterogeneous computational resources to evaluate its hypotheses before dedicated hardware is available. No other cloud provider currently offers the breadth of resource types needed:

| Provider | ARM CPU | HPC Cluster | FPGA Fabric | Integrated Research Credits |
|----|----|----|----|----|
| **AWS** | Graviton | Hpc7g, Hpc6id | F1 instances | Activate program |
| NVIDIA | — | GPU-focused | — | Inception (GPU-limited) |
| Other Clouds | Limited/None | Limited | None | Varies |

NVIDIA, while dominant in GPU computing, is ultimately constrained to a single resource type. What SSCCS requires—simultaneous access to ARM CPUs for compiler optimization, HPC clusters for large-scale simulation, and FPGA fabric for hardware validation—can only be delivered by AWS’s heterogeneous infrastructure portfolio, on a pay-as-you-go basis that matches research usage patterns.

| Requirement | AWS Capability | Relevance |
|----|----|----|
| Massively Parallel Compilation | Graviton’s price‑performance enables cost‑effective grid search over compiler configuration space | Allows testing SSCCS’s portability across ISAs, starting with ARM |
| Cross‑Architecture Validation | Diverse instance portfolio (Graviton, x86, HPC) | Assesses generality of SSCCS abstraction layers |
| Pre‑Silicon Emulation | HPC clusters provide scale to emulate PIM/CIM behaviour | De‑risks future hardware exploration |
| FPGA Hardware Validation | F1 instances enable synthesis and testing of Verilog RTL translated from verified RISC‑V assembly | Final hardware verification step before potential tape‑out |
| Reproducibility & Auditability | Consistent cloud infrastructure supports repeatable experiments | Aids research into computational auditability |
| Research Dissemination | Global reach and visibility | Helps share results with the wider community |

![](aws_files/figure-html/fig-aws-value-output-1.svg)

Figure 3: Why AWS: infrastructure capabilities aligned with SSCCS validation needs

## Strategic Workstream A: Autonomous Compiler Optimization on Graviton

### The Challenge: Configuration Space Explosion

The SSCCS compiler must map high‑dimensional logical schemas to physical hardware topologies. This leads to a large configuration space:

| Parameter | Search Space | Impact |
|----|----|----|
| Fusion Aggressiveness (\\\alpha\\) | {0.2, 0.4, 0.6, 0.8, 1.0} | Controls operator fusion for latency reduction |
| Layout Strategy (\\\lambda\\) | {RowMajor, ColumnMajor, SpaceFillingCurve, Hierarchical} | Determines physical memory locality |
| Precision Mode (\\\pi\\) | {fp64, fp32, fp16, int8_mixed} | Balances numerical fidelity vs. energy/throughput |
| Liveness-Guided Allocation | Linear-scan buffer allocation | Aims to reduce peak memory by 30–48% through reuse |

Manual tuning is impractical. We propose to develop an LLM‑driven autotuning agent and evaluate it primarily on AWS Graviton.

### The Approach: Graviton-Based Agentic Loop

![](aws_files/figure-html/fig-agent-loop-output-1.svg)

Figure 4: Closed-loop autonomous compiler optimization on AWS Graviton

#### Workflow

1.  Hypothesis Generation: Agent proposes optimization pass sequences based on Schema structure.
2.  Automated Evaluation: Trigger parallel compilation jobs on Graviton clusters.
3.  Metric Collection: Measure CEI, FGR, latency, energy per configuration.
4.  Feedback Integration: Store results in Graph-RAG knowledge base for iterative improvement.

#### Key Metrics

| Metric | Formula | Purpose |
|----|----|----|
| Compilation Efficiency Index (CEI) | `inference_speedup / compilation_time` | Quantifies return on compile‑time investment |
| Fusion Gain Ratio (FGR) | Estimated cost reduction from fusion passes | Diagnostic for fusion effectiveness |
| Buffer Reduction Ratio (\\\rho\_{buf}\\) | Memory savings from liveness-guided reuse | Evaluates memory optimization impact |

#### Why Graviton?

| Factor | Rationale |
|----|----|
| Cost Efficiency | 40% better price‑performance vs. x86 allows large‑scale experiments within research budgets |
| Architecture Diversity | Testing on ARM provides a non‑x86 benchmark to evaluate portability |
| Scalability | High core density enables parallel search across many compiler instances |
| Alignment | Graviton instances align with energy‑efficient computing research, an area of interest to AWS |

## Strategic Workstream B: Hardware Emulation at Scale on AWS HPC

### The Challenge: Pre‑Silicon Validation - Without Dedicated Hardware Acceleration

SSCCS aims to achieve \\O(\log^ N)\\ (Iterated Logarithm), \\O(\log \log N)\\ or \\O(\log N)\\ latency for operations that are \\O(N)\\ in von Neumann systems. Evaluating this hypothesis requires simulating:

- Massive Segment Grids: Billions of immutable coordinates representing workloads such as climate models, protein structures, or graph analytics.
- Field Propagation: Constraint resolution across distributed memory nodes without explicit data movement.
- PIM/CIM Behaviour: Emulating processing‑in‑memory logic on standard DRAM hierarchies.
- Determinism Investigation: Assessing reproducibility of results (targeting coefficient of variation \< 2.5%) across many parallel observations.

![](aws_files/figure-html/fig-emulation-scale-output-1.svg)

Figure 5: AWS HPC clusters as the validation fabric for pre-silicon SSCCS emulation

### The Approach: AWS HPC Clusters

We seek access to AWS HPC instances (e.g., Hpc7g, Hpc6id) to:

1.  Distributed Emulation: Run the SSCCS runtime across many nodes, treating the cluster as a single structural manifold.
2.  Stress Testing: Examine the “stationary data” hypothesis under high concurrency, using AWS CloudWatch telemetry as energy proxy.
3.  Benchmarking: Generate comparative data against traditional HPC workloads (MPI/OpenMP) to quantify potential latency and energy improvements.
4.  Buffer Allocation Testing: Implement linear‑scan buffer allocators on simulated constrained‑memory environments (simulating NPU SRAM limits).

#### Value for AWS

| Benefit | Description |
|----|----|
| Research Showcase | Demonstrates AWS’s infrastructure supporting fundamental computing research. |
| Sustainability | If energy gains are confirmed, validations on AWS highlight the role of efficient cloud resources. |
| Community Engagement | May attract researchers interested in alternative architectures to the AWS ecosystem. |
| Long‑Term Insights | Results could inform future custom silicon considerations, though such outcomes are speculative at this stage. |

## Strategic Workstream C: FPGA Golden Reference Validation on AWS F1

### The RISC‑V Assembly as Golden Reference Model

Our current `observe_full.S` RISC‑V assembly is more than test code—it serves as a **golden reference model** for hardware synthesis. Every constraint function (`ck_even`, `ck_range_0_10`, `compose_and`, `compose_or`, etc.) is already branchless, constant‑time, and directly translatable to Verilog RTL. The assembly has been validated on x86‑64 CI via pure‑Rust fallbacks that mirror the assembly logic exactly.

![](aws_files/figure-html/fig-golden-ref-output-1.svg)

Figure 6: From Rust through RISC‑V assembly to FPGA validation: the golden reference path

#### Example: From Assembly to Verilog

**RISC‑V Assembly (source):**

``` asm
ck_even:
    ld      t0, 0(a0)
    andi    t0, t0, 1
    xori    a0, t0, 1
    ret
```

**Verilog RTL (direct translation):**

``` verilog
module ck_even (
    input  wire [63:0] coord,
    output wire        result
);
    assign result = (coord[0] ^ 1'b1);  // even check: LSB == 0 → 1
endmodule
```

This 1:1 mapping between assembly and Verilog means hardware validation is a straightforward translation step—not a re‑design. Every constraint function in the assembly has already been verified in software on CI before it reaches FPGA.

### The Approach: AWS F1 FPGA Instances

We seek access to AWS F1 instances to:

1.  Translate Assembly → Verilog: Convert verified RISC‑V constraint functions to Verilog RTL.
2.  Synthesize on AWS FPGA Developer AMI: Use Vivado toolchain to generate bitstreams.
3.  Deploy to F1: Upload and execute on actual FPGA hardware.
4.  Compare Outputs: Validate FPGA results against both Spike (RISC‑V simulator) output and Rust fallback output.
5.  Prove Equivalence: Demonstrate that the software golden reference model and FPGA hardware implementation produce identical results across all constraint combinations.

This completes the `.ss → .S → SSC` compilation pipeline: Field definitions (`.ss`) are translated to assembly (`.S`), then compiled to Structurally Signed Code (SSC) executing on verified hardware.

#### Value for AWS

| Benefit | Description |
|----|----|
| FPGA Customer Story | Demonstrates AWS F1 as a viable platform for pre‑silicon validation of novel compute architectures |
| Software‑Defined Hardware Narrative | “From Rust to FPGA” is a compelling narrative for AWS’s custom silicon messaging |
| Open‑Source Alignment | The golden reference methodology is publishable as an open validation standard |

## Proposed Collaboration Model: Exploring a Partnership

We envision a phased collaboration that could, upon mutual agreement, lead to a joint announcement acknowledging AWS’s role in this research.

### Phase Deliverables

| Phase | SSCCS Plans | AWS Support Sought |
|----|----|----|
| Phase 1 | Functional autonomous optimizer; preliminary benchmark data; validated liveness analysis module; Verilog RTL for core constraint functions | Cloud credits; Graviton/HPC/FPGA access; technical onboarding |
| Phase 2 | Optimized SSCCS compiler stack with composable passes; FPGA‑validated observation engine; joint technical publication: “AI‑Driven Compiler Design on AWS Graviton” | SA support; beta feature access; architecture review sessions |
| Phase 3 | Potential joint announcement; open specification release; FPGA golden reference validation complete; collaboration continuation | Co‑marketing and press support (if mutually agreed) |

## Immediate Request & Next Steps

To explore this collaboration, we specifically request:

1.  Introduction to the AWS Activate Team: To discuss research credits for Graviton, HPC, and FPGA usage (non‑production).
2.  Technical Liaison: An introductory call with an AWS solutions architect familiar with HPC workloads, Graviton‑based AI inference, and FPGA development, to advise on optimal instance selection.
3.  Strategic Discussion: A conversation about potential collaboration pathways and how AWS’s involvement might be acknowledged in future publications.
4.  FPGA Access: Discussion of F1 instance access and FPGA Developer AMI for the golden reference validation workstream.

We believe this work could be mutually beneficial, and we are eager to begin discussions. Our team has a published formal model and validated RISC‑V assembly golden reference; cloud‑scale infrastructure would significantly accelerate the research.

------------------------------------------------------------------------

This document serves as the homepage for the SSCCS × AWS collaboration discussions. It will be updated to reflect:

- Progress on phases 1–3
- Joint technical publications and benchmark results
- FPGA validation results and golden reference methodology
- Details of any joint announcements or consortium activities
- New validation domains and hardware targets