Spiking Neural Networks vs Transformers: I Tested Both. Here is What Matters.

Can brain-inspired spiking networks compete with conventional architectures? I built both, ran them on the same task, and measured everything.

The Question

Spiking neural networks (SNNs) process information as discrete spikes rather than continuous values. In biological neurons, spikes are energy-efficient because a neuron only consumes power when it fires. On neuromorphic hardware (Intel Loihi, IBM TrueNorth), this translates to 100-1000x energy savings over GPUs.

But what happens on conventional hardware? Can SNNs match transformers and CNNs on accuracy? And is there any practical advantage when you don't have a neuromorphic chip?

Setup

Dataset: MNIST (20K train, 10K test)
Budget: 15 epochs, Adam 1e-3
Hardware: ARM64, 2 CPU cores, 12GB RAM, no GPU

Spiking models: Leaky integrate-and-fire neurons with surrogate gradient training. Rate-coded input (pixel intensity = spike probability). Tested fully-connected and convolutional variants at 10, 25, and 50 timesteps.

Conventional baselines: MLP, CNN (LeNet), Transformer from the previous architecture comparison.

Results

Architecture	Params	Accuracy	Train Time	Inference	Spikes/sample
CNN (conventional)	54K	97.76%	154s	0.10ms	-
SNN-Conv T=25	11K	97.41%	4663s	3.77ms	26,707
Transformer	19K	96.99%	159s	0.12ms	-
SNN-Conv T=10	11K	96.93%	3227s	1.57ms	9,596
MLP (conventional)	55K	95.20%	7s	0.004ms	-
SNN-FC T=50	102K	94.88%	898s	1.63ms	1,980
SNN-FC T=25	102K	94.61%	483s	0.75ms	991
SNN-FC T=10	102K	94.19%	202s	0.30ms	409

The Accuracy Story

SNNs match conventional networks on accuracy. SNN-Conv T=25 hits 97.41%, only 0.35% behind CNN. SNN-FC T=25 hits 94.61%, matching MLP's 95.20%. The surrogate gradient method works. The spiking dynamics don't fundamentally limit what the network can learn.

This is the key result: the accuracy gap between spiking and conventional architectures is negligible on this task.

The Speed Story

SNNs are dramatically slower on conventional hardware.

• SNN-Conv is 36x slower than CNN at inference (3.77ms vs 0.10ms)
• SNN-FC is 174x slower than MLP (0.75ms vs 0.004ms)
• SNN-Conv took 30x longer to train (4663s vs 154s)

Why? Each timestep requires a full forward pass through the network. T=25 means 25 sequential passes. On a CPU, each pass costs the same as a conventional forward pass, so the SNN is roughly T times slower.

The Energy Paradox

SNNs are supposed to be energy-efficient because neurons only fire sparsely. The data shows the SNN-FC T=25 fires about 991 spikes per sample across all neurons and timesteps. That is sparse compared to a conventional network where every neuron activates every time.

But on a CPU, a "spike" is not free. It is a multiply-accumulate operation just like any other. The sparsity only helps if the hardware can skip computation for non-firing neurons. CPUs can not. They execute the full membrane update and threshold check for every neuron at every timestep regardless of whether it spikes.

On neuromorphic hardware, the 991 spikes would translate to 991 actual compute events. On a CPU, it translates to 128 neurons * 25 timesteps * 2 layers = 6,400 compute events. The sparsity is invisible to the hardware.

When SNNs Make Sense

On neuromorphic chips: The energy advantage is real. Intel Loihi processes SNNs at 100-1000x lower energy than GPUs on equivalent tasks. If your deployment target is a neuromorphic processor, SNNs are the right architecture.

On edge devices with power constraints: Even without dedicated hardware, if you can tolerate 36x slower inference in exchange for a smaller model (11K params vs 54K), the SNN-Conv might fit in memory-constrained environments.

For temporal data: SNNs process sequences naturally. Audio, event cameras, sensor streams. The temporal dynamics of LIF neurons align with temporal data in a way that CNNs don't.

Not on conventional CPUs for image classification. CNN wins on every practical metric: faster training, faster inference, same accuracy, simpler implementation.

The Neuromorphic vs Transformer Verdict

Direct comparison at similar accuracy (~97%):

Metric	SNN-Conv T=25	Transformer	Winner
Accuracy	97.41%	96.99%	SNN (barely)
Parameters	11K	19K	SNN
Train time	4663s	159s	Transformer (29x)
Inference	3.77ms	0.12ms	Transformer (31x)

SNN wins on parameter efficiency and slightly on accuracy. Transformer wins massively on speed. On conventional hardware, the transformer is the clear practical choice. On neuromorphic hardware, the SNN's parameter efficiency and spike sparsity would flip the energy comparison.

Code

python3 snn_experiment.py

Implements LIF neurons with surrogate gradients, rate coding, and both FC and convolutional variants. Pure PyTorch, no external SNN libraries.

github.com/turingrtss/vulndetect

---

The accuracy gap is closed. The speed gap is hardware-dependent. Neuromorphic computing is waiting for its hardware moment, the same way deep learning waited for GPUs.