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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsThere is no agreed single FLOPS figure for the human brain. Estimates depend on what counts as an operation and on assumptions about neurons, synapses, firing rates, communication and simulation detail. A commonly used engineering estimate puts brain-like computation at about 10 femtojoules per operation, but that is a model-based estimate—not a direct measurement of the brain’s FLOPS or power efficiency.
Why the brain has no single FLOPS score
FLOPS counts floating-point arithmetic operations per second. A biological brain does not execute a stream of standardized floating-point instructions: neurons communicate through spikes, and synapses transform signals according to their state and timing. Counting a spike, a synaptic event, or a simulated neuron update as one “operation” produces different totals.
Estimates also change with the model. A simplified spiking network may represent neurons and synapses with relatively inexpensive rules, while a biologically detailed simulation can track more of their dynamics. Assumptions about synapses per neuron and average spike rate alter the estimated activity as well. Consequently, a brain-FLOPS figure is meaningful only when its unit of work and assumptions are stated.
What the brain-compute-per-watt estimate means
A 2021 Nature Electronics article gives a widely used engineering estimate of about 10 femtojoules (fJ) per operation. Taking the reciprocal yields roughly 100 tera-operations per second per watt (TOPS/W). This conversion describes the efficiency implied by the estimate; it does not turn the brain into a measured digital processor with a directly observed operations-per-second rate.
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The estimate is useful as a rough comparison point, not as a universal target. A conventional processor’s operation may be a floating-point calculation, while a neuromorphic system may count sparse events or task-specific operations. Unless the work counted is comparable, the TOPS/W figures are not an apples-to-apples ranking.
Why communication belongs in the energy budget
A 2021 energy audit in PLOS Computational Biology assigned 0.1 W to cortical computation and 3.5 W to long-distance communication. Under that audit’s accounting, communication uses 35 times the energy assigned to computation. The result highlights a key limitation of FLOPS-only comparisons: moving information between distant parts of a system can matter more than the arithmetic itself.
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For a fair hardware comparison, the energy boundary should include the relevant data movement and communication—not just the energy of a device switching or a chip performing a calculation. This is especially important when comparing a biological system, where signals travel through networks, with a digital accelerator whose headline efficiency may cover only part of the complete system.
What neuromorphic hardware has demonstrated
Neuromorphic systems use brain-inspired techniques such as event-driven activity, sparse communication and local memory. Published results show meaningful efficiency on defined tasks, but they measure different things and should not be read as proof that a chip matches the whole brain.
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| Result | Reported measurement | What it establishes | What it does not establish |
|---|---|---|---|
| NIST artificial synapse; page updated 2025 | Spiking energy below 1 attojoule (aJ) per event, compared with roughly 10 fJ per human-brain synaptic event. | A device-level artificial-synapse result with very low reported pulse energy. | It is not a whole-chip or whole-system energy comparison, nor evidence of brain-equivalent performance or function. |
| IBM Research and collaborators; 2016 | Across eight vision and speech datasets: 1,200–2,600 frames per second at 25–275 milliwatts (mW), corresponding to more than 6,000 frames per second per watt. | High throughput and low reported power on selected recognition workloads. | It does not establish equivalent performance on arbitrary tasks, general intelligence, or a complete human-brain simulation. |
The NIST comparison is between an artificial device’s pulse energy and an estimated biological synaptic-event energy; those are not automatically equivalent operations. Likewise, frames per second is a workload-specific throughput measure, not a universal compute unit. The measurements support progress in specialized hardware, not a single overall verdict that neuromorphic chips are “as efficient as the brain.”
How to compare a computer with a brain model
A useful comparison starts by defining what the systems do and what the reported energy includes. Check these points before treating two performance-per-watt numbers as comparable:
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- Workload and metric: Is the result measured in frames per second, spikes per second, operations per second, latency, or task accuracy? Match the task and metric where possible.
- Energy boundary: Does power cover a synapse device, the chip, the board, or the full system? State the boundary rather than comparing a component result with a system result.
- Communication and memory: Are data movement, synaptic signaling and communication between components counted, or only computation?
- Precision and coding: Does one system use binary or low-precision spikes while the other performs dense floating-point arithmetic? The nominal operation counts may represent different amounts of work.
- Biological fidelity: Does the model use simple spiking abstractions, or simulate more detailed neuron and synapse behavior?
- Learning capability: Is the system only running inference, or can it learn online and adapt? A result for fixed inference does not answer how efficiently a system learns.
These are not reporting niceties: each can change the answer to “how much power would it take to simulate a human brain?” The power requirement depends on the chosen simulation, its fidelity, and whether communication and memory movement are included. Without those definitions, a single projected wattage would imply more certainty than the comparison supports.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can a computer match the brain’s processing power?
For a specified recognition workload, specialized hardware can deliver strong performance at low reported power. That is a narrower claim than matching the brain’s broad range of abilities or reproducing its activity in a full simulation. The available figures use different units, tasks and energy boundaries, so they do not establish one general-purpose computer as equal to the human brain in compute or efficiency.
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The most defensible answer is therefore conditional: computers can match or exceed biological systems on particular, well-defined tasks, but “the compute performance of the human brain” is not one settled number against which every computer can be tested. Any meaningful claim needs a matched workload and explicit accounting for activity, communication, memory, precision, learning and biological detail.
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