The Sparsity Tax: Weight Sparsity Trade-offs in Event-Driven SIMD and SIMT Neuromorphic Cores

Mattias Westerink, Sameed Sohail, Berend-Jan van der Zwaag, Sabih Gerez, Amirreza Yousefzadeh

Event-driven neuromorphic inference exploits activation sparsity by updating neuron state only on spikes. However, weight sparsity introduces irregular gather-style updates that undermine lockstep Single Instruction Multiple Data (SIMD) execution. We call the resulting overheads in control, metadata, and memory activity the sparsity tax. This paper quantifies that tax by comparing three closely related accelerators integrated into one neuromorphic core: (i) baseline lockstep SIMD, (ii) bitmap-gated Sparse-SIMD that selectively disables lanes without compressing weights, and (iii) a Single Instruction Multiple Threads (SIMT) style design with per-PE address generation and run-length coded sparse weights. Using an RTL-to-gates flow in GF22FDX+ and activity-driven energy estimation, we evaluate event-driven neural network inference across varying post-training pruning levels. Results show that total core area changes are insignificant because SRAM dominates area, while performance and energy strongly depend on how sparsity is handled: SIMD and Sparse-SIMD exhibit near-constant throughput, Sparse-SIMD achieves limited energy savings due to bitmap and dense-storage overheads, and SIMT provides the strongest energy scaling and substantial speedups at high sparsity, albeit with sublinear gains due to metadata reads, load imbalance, and sparsity-independent phases. The hardware code for the proposed architectures and experiments is publicly accessible for research purposes.