When Does One-Shot Pruning Beat Iterative Optimisation? Second-Order Correction for Sparse LLMs on Neuromorphic Hardware
Kimia Gholami et al., NeurIPS 2026 submission

Performance

Metric	Value
WikiText-2 PPL (dense)	14.18
WikiText-2 PPL (sparse)	1,952
Sparsity	80%
ARC-Easy	0.269
ARC-Challenge	0.222
HellaSwag	0.258
PIQA	0.527
WinoGrande	0.491
LAMBADA	0.000
Average	0.294

Best PPL among all methods at 80% sparsity on HGRN-1.3B (SparseGPT: 2,811; AWP: 17,756; Wanda: 75,620; RIA: 26,817). At this extreme compression level all methods produce largely incoherent output; this model is provided primarily for hardware sparsity efficiency benchmarking rather than language quality.

Sparsity structure

Semi-structured sparsity: exactly k = floor(d_in * 0.8) weights are zeroed per output row in every linear layer. Zero weights map directly to zero spike events on Intel Loihi.

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python

import torch, fla
from fla.models.hgrn import HGRNConfig, HGRNForCausalLM
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer

AutoConfig.register("hgrn", HGRNConfig, exist_ok=True)
AutoModelForCausalLM.register(HGRNConfig, HGRNForCausalLM, exist_ok=True)

model_id = "ikimyaii/hgrn-1.3B-obs-cancel-block-80pct"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
).cuda()

Install FLA first:

pip install flash-linear-attention

ikimyaii/hgrn-1.3B-obs-cancel-block-80pct-uniform

HGRN-1.3B OBS-cancel-block 80% Sparse

Performance

Sparsity structure

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ikimyaii/
hgrn-1.3B-obs-cancel-block-80pct-uniform