The Joules-per-Solution Metric: Definition, Measurement Protocol, and Anti-Gaming Provisions for Honest Computational Benchmarking

In the paper's words

We define and formalize the joules-per-solution (J/S) metric as a universal, cross-domain measure of computational energy efficiency. The metric accounts for six energy components: computation, memory, I/O, cooling, power conversion, and amortized manufacturing. We provide a closed-form definition, a five-phase measurement protocol, and a suite of anti-gaming provisions including pre-registration, adversarial validation, Pareto frontier reporting, component audit, and a living benchmark protocol.

The abstract, verbatim, and its opening sentence is the claim. Every line below is quoted from the text.

Numbers it reports

1. Neuromorphic computing claims a 100–1,000× energy advantage over digital CMOS for specific task classes, operating at potentially $10$–$10^2$ times Landauer — three to five orders of magnitude more efficient than GPUs — but lacks standardized benchmarking to substantiate these claims.

   from 1.1 The Fifteen Orders of Magnitude

2. Is a Loihi 2 neuromorphic chip more energy-efficient than an NVIDIA H100 GPU for real-time video object detection at 95% accuracy?

   from 2.3 The Joules-per-Solution Gap

3. For cryptographic systems, the energy of transmitting ciphertexts is included — a critical factor for post-quantum cryptography where ciphertexts are 10–100× larger than classical equivalents [@1903.04570].

   from 3.3 Total System Energy Accounting

4. For air-cooled data centers (PUE $\approx$ 1.1–1.3), cooling adds 10–30% overhead.

   from 3.3 Total System Energy Accounting

5. In biology, it is metabolic cost per adaptive behavior — the human brain achieves $10^{16}$ synaptic operations per second on 20 W, approximately $2 \times 10^{-15}$ J per "operation," three orders of magnitude more efficient than the best CMOS [@2604.04727].

   from 5.3 Cross-Domain Consilience

6. For data-intensive tasks (AI training, database queries), memory energy can exceed computation energy by 2–5×.

   from 3.3 Total System Energy Accounting

How it is done

1. Author: QNFO | Date: 2026-07-27 | Status: Draft — Phase 5 Series: Joules-per-Compute Universal Benchmark (JPCUB) — Paper P0 License: QNFO Unified License Agreement (QNFO-ULA)

   from The Joules-per-Solution Metric: Definition, Measurement Protocol, and Anti-Gaming Provisions for Honest Computational Benchmarking

2. We provide a formal definition with closed-form measurement equations, a measurement protocol covering computation, cooling, power conversion, networking, and amortized manufacturing energy, and a comprehensive anti-gaming framework with adversarial validation, pre-registration requirements, and Pareto frontier reporting.

   from Abstract

3. For any computational task $T$ with a correctness threshold $\varepsilon$, the computational advantage of system $A$ over system $B$ is defined as: $$\text{Advantage}_T(A, B) = \frac{E_B(T, \varepsilon)}{E_A(T, \varepsilon)}$$ where $E_X(T, \varepsilon)$ is the total system-level energy consumed by system $X$ to produce a solution to $T$ within correctness bound $\varepsilon$.

   from 1.3 Core Claim

4. The Landauer bound [established] (1961): any logically irreversible operation — such as erasing a bit — dissipates at minimum $kT \ln 2$ joules of energy as heat.

   from 2.1 Physical Foundations

What would falsify it

1. This paper proposes [my conjecture] the joules-per-solution metric — the total system-level energy required to produce a correct solution to a computational task at a specified correctness threshold — as the universal, physics-grounded, falsifiable arbiter of computational advantage.

   from Abstract

2. It would be disconfirmed if, for a representative sample of $\geq 100$ real-world computational tasks spanning $\geq 4$ of the 7 paradigm domains, a different metric (wall-clock time, FLOPs, or quantum volume) predicted procurement outcomes more accurately than joules-per-solution, where procurement outcome is defined as the system chosen by an independent evaluator given total cost of ownership data including energy costs.

   from 1.3 Core Claim

3. Each prediction has a defined checkpoint date and falsification condition.

   from 6. Calibration Register [speculative]

Where the claim stops

1. At the other extreme, fault-tolerant gate-model quantum computers, burdened by cryogenic cooling at millikelvin temperatures, control electronics operating at gigahertz frequencies, and error-correction overheads of $10^2$ to $10^6$ physical qubits per logical qubit, consume approximately $10^{12}$ to $10^{15}$ times the Landauer limit per logical operation.

   from 1.1 The Fifteen Orders of Magnitude

2. The least energy-efficient paradigm — gate-model quantum computing — has absorbed approximately $ 35 billion in global investment, while the most energy-efficient paradigms — neuromorphic and thermodynamic computing — receive orders of magnitude less funding.

   from 1.1 The Fifteen Orders of Magnitude

3. The universe does not care about FLOPs.

   from 1.2 The Blind Spot

Where to read

1. Abstract262 words · 1 min
2. 1. Introduction640 words · 3 min
3. 2. Background and Prior Work584 words · 3 min
4. 3. The Joules-per-Solution Metric [my conjecture]826 words · 4 min
5. 4. Anti-Gaming Provisions [speculative]421 words · 2 min
6. 5. Cross-Domain Comparison Framework [speculative]357 words · 2 min
7. 6. Calibration Register [speculative]168 words · 1 min
8. 7. Limitations and Known Objections463 words · 2 min
9. 8. Conclusion182 words · 1 min
10. Declarations128 words · 1 min