The Energy Model - THRINDEX

The formula

energy_pJ = synaptic_ops × coefficient_pJ

Where:

synaptic_ops = total number of spike-weighted fanout operations across all timesteps
coefficient_pJ = energy per synaptic operation (default: 1 pJ)

A synaptic operation (syn-op) occurs when a pre-synaptic neuron fires and its spike propagates to a post-synaptic neuron through a weighted connection. If a neuron with 512 outgoing connections fires once, that is 512 syn-ops.

How THRINDEX counts syn-ops

The simulator counts syn-ops from the spike raster. For a Dense layer with in_features inputs and out_features outputs:

syn-ops at timestep t = (number of input neurons that spiked at t) × out_features

Over all T timesteps:

total_syn-ops = Σ_t Σ_n spike[t, n] × out_features_of_n's_downstream_layer

This is reported in the thrindex run transcript:

synaptic_ops: 124800

The coefficient

1 pJ per syn-op is a literature-derived estimate for digital synchronous neuromorphic chips (Loihi-class). It is not a measurement from any specific chip or workload. Published figures from the literature:

System	syn-op energy	Source
Intel Loihi	~23 pJ/syn-op	Davies et al. (2018) at 130 nm
Intel Loihi 2	~1–3 pJ/syn-op	Orchard et al. (2021), 7 nm
IBM TrueNorth	~26 pJ/syn-op	Merolla et al. (2014)
SpiNNaker 2	~5 pJ/syn-op	Mayr et al. (2019)

The THRINDEX default of 1 pJ is consistent with modern 7 nm digital neuromorphic chips. It is deliberately conservative.

Comparing to a GPU baseline

A GPU running a conventional ANN with the same task uses floating-point multiply-accumulate (MAC) operations. A modern GPU does a MAC in approximately 0.1–0.5 pJ, but every neuron performs a MAC on every forward pass — there is no sparsity. An SNN on neuromorphic hardware only spends energy when a neuron spikes. If the average firing rate is 5% (5 spikes per neuron over 100 timesteps), the active computation is 5% of what a dense ANN would do. The comparison is:

GPU energy ≈ total_params × MAC_cost
SNN energy ≈ total_syn-ops × syn-op_cost
           = total_params × firing_rate × syn-op_cost

At 5% firing rate and 1 pJ/syn-op vs 0.3 pJ/MAC, the SNN uses ~17× less energy for the same model size. The ratio scales linearly with firing rate — a sparser network is a more efficient one.

What this number is not

It is not a silicon measurement. No chip was powered on to produce this number.
It is not a claim about latency, throughput, or system-level power.
It does not include memory access energy, chip idle power, or host interface overhead.
It will not match a datasheet. Datasheets measure different operating conditions.

The energy model exists to make design tradeoffs visible during model development — not to make marketing claims.

Changing the coefficient

thrindex run model.thx --energy-coeff 3.0

Sets the coefficient to 3 pJ/syn-op. Use this when you have a measurement from your specific target chip. The formula is unchanged; only the multiplier changes.

​The formula

​How THRINDEX counts syn-ops

​The coefficient

​Comparing to a GPU baseline

​What this number is not

​Changing the coefficient