Bishop: Sparsified Bundling Spiking Transformers on Heterogeneous Cores with Error-Constrained Pruning

• URL: http://arxiv.org/abs/2505.12281v1
• Date: Sun, 18 May 2025 07:42:58 GMT
• Title: Bishop: Sparsified Bundling Spiking Transformers on Heterogeneous Cores with Error-Constrained Pruning
• Abstract summary: We introduce the concept of Token-Time Bundle (TTB) accelerator, a container that bundles spiking data of a set of tokens over multiple time points.<n>Bishop is the first dedicated hardware computation architecture and HW/SW co-design framework for spiking-based workloads.
• Score: 5.421489676274393
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present Bishop, the first dedicated hardware accelerator architecture and HW/SW co-design framework for spiking transformers that optimally represents, manages, and processes spike-based workloads while exploring spatiotemporal sparsity and data reuse. Specifically, we introduce the concept of Token-Time Bundle (TTB), a container that bundles spiking data of a set of tokens over multiple time points. Our heterogeneous accelerator architecture Bishop concurrently processes workload packed in TTBs and explores intra- and inter-bundle multiple-bit weight reuse to significantly reduce memory access. Bishop utilizes a stratifier, a dense core array, and a sparse core array to process MLP blocks and projection layers. The stratifier routes high-density spiking activation workload to the dense core and low-density counterpart to the sparse core, ensuring optimized processing tailored to the given spatiotemporal sparsity level. To further reduce data access and computation, we introduce a novel Bundle Sparsity-Aware (BSA) training pipeline that enhances not only the overall but also structured TTB-level firing sparsity. Moreover, the processing efficiency of self-attention layers is boosted by the proposed Error-Constrained TTB Pruning (ECP), which trims activities in spiking queries, keys, and values both before and after the computation of spiking attention maps with a well-defined error bound. Finally, we design a reconfigurable TTB spiking attention core to efficiently compute spiking attention maps by executing highly simplified "AND" and "Accumulate" operations. On average, Bishop achieves a 5.91x speedup and 6.11x improvement in energy efficiency over previous SNN accelerators, while delivering higher accuracy across multiple datasets.

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