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Software-level alignment guardrails (RLHF, system prompts, container sandboxes) are inherently vulnerable to jailbreaks, prompt injection, and stochastic hallucinations. When frontier autonomous agents begin directly controlling physical actuators, drones, robotics, or critical network tools, a software crash or model jailbreak can cause runaway actuation. Furthermore, deriving gradients and evaluating continuous control surfaces on standard GPUs forces models across high-latency memory buses to off-chip DRAM, introducing 12 ms to 250 ms of operating system tail jitter and consuming 75W to 300W of power.
Mavericks Alpha Signal LLC built and verified a synthesizable digital silicon microarchitecture (omega_mollifier_core paired with sre_watchdog_shield) that moves containment to the physical hardware layer. We replaced discrete finite differences and recursive software Autograd tapes with continuous Sobolev-space Friedrichs mollification in signed Q1.30 fixed-point silicon. In a single 66-cycle forward pass inside an on-die 16 KB SRAM buffer, the core computes exact weak derivatives with 0.00 Bytes of external DRAM access, cutting memory buffering by 91.25% at 18.4 mW active core power. Crucially, our hardware watchdog acts as an unbypassable, ISO 26262 ASIL-D / DO-254 DAL-A compliant circuit breaker: if an agent stalls, enters a runaway recursive loop, or tries to assert out-of-distribution actuation commands, the hardware physically clamps the bus and forces safe-state shutdown within strictly 50.0 ms. Because this boundary is enforced at the physical gate level, no software prompt or adversarial model weight can bypass it.
Our goal is to build an open-science, physical hardware evaluation testbed demonstrating that embodied AI agents and robotics can be bound by deterministic, sub-microsecond physical silicon constraints.
To achieve this, we will:
Deploy our verified SystemVerilog core (omega_mollifier_core) and supervisory watchdog (sre_watchdog_shield) onto physical AMD Virtex UltraScale+ and Versal FPGA carrier boards in our Charlotte clean-room lab.
Interface the FPGA streaming bus directly with an open-weights embodied agent policy (such as Open X-Embodiment or RT-2 robotics control feeds).
Benchmark physical-layer fault injection: simulate adversarial prompt overrides and command loops to demonstrate empirical 50.0 ms hard-cutoff transitions on physical actuators.
Release the open hardware evaluation wrappers and benchmarking scripts so AI safety researchers can independently verify deterministic hardware containment.
We have already verified the mathematical core in pre-silicon EDA: static timing analysis closed at 1.2 GHz across all 16 PVT corners on a 5nm FinFET reference flow, 134/134 formal assertions proven in Cadence JasperGold, 512 randomized UVM burst tests passed, and bit-exact parity achieved across 50,000 vectors against double-precision software (MSE <= 3.47e-10).
We will know this next hardware milestone worked when:
The hardware testbed runs live on physical FPGA silicon, ingesting streaming agent control vectors over an AXI4-Stream bus without dropping packets.
The physical oscilloscope and logic analyzer confirm a deterministic 264.0 ns state evaluation latency (+/-0.4 ns jitter @ 250 MHz; 204.8 ns @ 322 MHz) under continuous load.
Injected out-of-bounds agent actions trigger an unbypassable 50.0 ms physical bus clamp 100 out of 100 consecutive times without host CPU or operating system intervention.
Every dollar goes directly into physical lab fixtures and bench test hardware in our Charlotte facility:
$450: High-frequency SMA coaxial test cabling, DC power breakout headers, and SMA-to-BNC lab adapters for physical FPGA carrier test fixtures.
$350: PCIe 4.0/5.0 riser assemblies, high-speed loopback connectors, and passive heatsink mounts for benchtop hardware-in-the-loop validation.
$200: Cloud-hosted FPGA synthesis compute hours for running continuous automated formal verification testbenches. Total Initial Tranche: $1,000 (If funded up to $5,000–$10,000, the surplus directly funds dedicated carrier boards and shipping physical evaluation kits to independent AI safety researchers).
I am Jonathan R. Ament, Founder and Chief Silicon Architect of Mavericks Alpha Signal LLC. I bring over 20 years of experience designing mission-critical high-throughput deterministic data and algorithmic risk systems across tier-1 financial institutions (including JPMorgan Chase Private Bank and LPL Financial; FINRA CRD: 4578508, ORCID: 0009-0005-9058-9991). I used this background in high-volume stochastic systems to invent and implement the continuous Sobolev-space fixed-point microarchitecture in synthesizable SystemVerilog.
Track Record & Institutional Anchors:
100% corporate patent title recorded at the USPTO across Provisional Patent Application Nos. 64/127,500 and 64/128,324 (EPAS Reel 075667 / Frames 0383 & 0567), drafted by lead patent counsel Dr. David V. Sanker (SankerIP).
Open-science preprints and full verification datasets permanently indexed on CERN Zenodo (DOI 10.5281/zenodo.24071603 and DOI 10.5281/zenodo.24074626).
Active federal credentials: SAM.gov UEI: RA2FZ1JUGCK8, DLA CAGE Code: 26U94, and DoD SPRS CMMC Level 1 UID: S100083997.
Toolchain & Interface Latency: The most plausible technical challenge is interface friction when bridging high-level Python robotics frameworks to low-level FPGA AXI4-Stream buses. If software serialization delays mask hardware execution speed, the physical speedup may be harder to measure from high-level software. Mitigation: We bypass operating system drivers by using zero-copy DMA and raw Ethernet UDP framing directly into FPGA registers.
Ecosystem Inertia: AI developers are accustomed to pure software guardrails and may resist adding hardware co-processors. Mitigation: We frame our IP as a drop-in macro for edge ASICs and commercial FPGA cards, demonstrating that it cuts active power from 300W to 18.4 mW while adding safety as a built-in byproduct.
$0.00. Mavericks Alpha Signal LLC is 100% founder-bootstrapped. I have funded 100% of the mathematical formulation, RTL development, clean-room operations, and patent prosecution out of personal savings without outside dilutive equity or institutional venture backing.