Latency and accuracy tradeoffs in Spiking Neural Networks

Zhanglu Yan; Zixuan Zhu; Kaiwen Tang; Yuyang Cai; Qianhui Liu; Weng-Fai Wong

cs.LGSep 28, 2026

Latency and accuracy tradeoffs in Spiking Neural Networks

Authors: Zhanglu Yan, Zixuan Zhu, Kaiwen Tang, Yuyang Cai, Qianhui Liu, Weng-Fai Wong

Organizations: Department of computer science, National University of Singapore · Shanghai Advanced Research Institute, Chinese Academy of Science · University of Chinese Academy of Sciences, Beijing · School of Artificial Intelligence, Shandong University

Abstract

Spiking neural networks are attractive for low-power speech command recognition, yet their latency has received far less attention than their energy efficiency, and their multi-timestep execution is widely assumed to make them slower than quantized neural networks. This paper challenges the assumption that more local timesteps necessarily imply higher network latency. By overlapping computation across adjacent layers at the timestep level, SNNs may complete execution in less time than comparable bit-serial QNNs. However, this overlap relies on spikes firing on incomplete inputs, and a spike once generated cannot be withdrawn, so its error persists and reduces accuracy. Waiting for more input before firing would seem to improve accuracy at the cost of reduced overlap. Yet we find and prove that this intuition fails at some layers, where even a small increase in waiting can change spike timing and downstream computation, making the network both slower and less accurate. We therefore propose a Pipeline Delay Search method which selects each layer's delay by balancing task-level accuracy gains against added network latency. We then adapt the selected configurations through spike-based quantization-aware training and bounded tuning of firing thresholds and initial membrane potentials. Together, these steps form Falcon, a framework for Fine-grained Analysis of Latency and Controlled firing which systematically analyzes and optimizes SNN latency under a spatial analog compute-in-memory mapping with shared digital engines. We evaluate Falcon on GSCV2 and SSC, achieving competitive accuracies of 96.31 and 83.02 at modeled network-core latencies of 119.64 and 124.00us, respectively. Together, our analysis and results show that SNNs can compute more yet finish faster, and wait longer yet predict worse, highlighting why Falcon matters for both latency and accuracy.

Figures & tables

Figure 1: Cross-timestep pipelining and firing delay in SNNs. (a) Different layers process different timesteps in parallel, allowing earlier completion. (b) Delay-controlled IF kernel with

\delta

sets how many input slots are accumulated before the first firing decision.

Figure 2: Effects of firing delay on Falcon-Large accuracy and latency.(a) Accuracy and latency under uniform delays. (b) Layerwise accuracy changes for delay increases $1\!\to\!2$ and $1\!\to\!3$ . (c) Three representative layer responses. (d) PDS candidate screening, with delay increases passing both score and accuracy-gain gates highlighted in green.

Figure 3: Delay search and training of Falcon-Large on GSC. (a) Validation accuracy and latency of Fastest, Balanced, and Accurate before SNN training. (b) Comparison with ten random delay schedules at the same core latency before SNN training. (c) Validation accuracy at each SNN training stage.

	SSC Falcon-Medium		GSC Falcon-Medium		GSC Falcon-Large
Configuration	Acc.(%)	latency( $\mu$ s)	Acc.(%)	latency( $\mu$ s)	Acc.(%)	latency( $\mu$ s)
Matched QNN	83.97	131.46	96.68	130.86	96.92	202.98
Full-lookahead	83.94	253.04	96.68	252.44	96.91	385.44
PDS–Fastest	81.24	116.42	96.11	115.82	95.46	180.42
PDS–Balanced	83.02	124.00	96.31	119.64	96.19	184.24
PDS–Accurate	83.02	129.70	96.39	129.04	96.52	201.16

Table 1: Test accuracy and modeled network-core latency.

Method	Params. (M)	Accuracy (%)	Latency ( $\mu$ s)
T-BSO Liang et al. (2025) , Spiking VGG-11	9.23	96.12	1714.88
SpikeSCR Wang et al. (2024a) , 1L-16-256 (short)	1.63	94.71	278.47
SpikeSCR, 1L-16-256	1.63	95.90	468.11
SpikCommander Wang et al. (2026) , 1L-16-256	1.12	96.71	487.97
SpikCommander, 2L-16-256 (short)	2.13	96.27	645.63
SpikCommander, 2L-16-256	2.13	96.92	940.70

Table 2: Comparison on GSC in terms of model size, accuracy, and latency. As most prior works do not report hardware latency, we reproduce the latency of several representative state-of-the-art SNN models with digital arrays of the same size of Falcon. Detailed implementations and latency reconstruction are provided in Appendix F . For Falcon, results are reported as Non-pipelined / Balanced .

	SSC		GSC
Method / Configuration	Params. (M)	Accuracy (%)	Params. (M)	Accuracy (%)
DCLS-Delays, 2L-2KC ( Hammouamri et al., 2024 )	1.40	$80.16\pm 0.09$	1.40	$95.00\pm 0.06$
DCLS-Delays, 3L-2KC	2.50	$80.69\pm 0.21$	2.50	$95.35\pm 0.04$
d-cAdLIF ( Deckers et al., 2024 )	0.70	$80.23\pm 0.07$	0.61	$95.69\pm 0.03$
SNN-Delays+ TR/NAR ( Zhang et al., 2024 )	2.50	81.02	2.50	95.62
CADAD, 3L ( Bai et al., 2026 )	0.60	$80.69\pm 0.24$	0.60	$95.58\pm 0.15$

Table 3: Comparison with prior SNN speech-recognition methods. Falcon reports Full-lookahead (direct conversion) / PDS–Accurate .“–” denotes an unreported or unverified value.

Appendix figures & tables15 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 4: Effects of firing delay on Falcon-Medium accuracy and latency (GSC).(a) Accuracy and latency under uniform delays. (b) Layerwise accuracy changes for delay increases $1\!\to\!2$ and $1\!\to\!3$ . (c) Three representative layer responses. (d) PDS candidate screening, with delay increases passing both score and accuracy-gain gates highlighted in green.

Figure 5: Effects of firing delay on Falcon-Medium accuracy and latency (SSC).(a) Accuracy and latency under uniform delays. (b) Layerwise accuracy changes for delay increases $1\!\to\!2$ and $1\!\to\!3$ . (c) Three representative layer responses. (d) PDS candidate screening, with delay increases passing both score and accuracy-gain gates highlighted in green.

Figure 6: Delay search and training of Falcon-Medium on GSC. (a) Validation accuracy and latency of Fastest, Balanced, and Accurate before SNN training. (b) Comparison with ten random delay schedules at the same core latency before SNN training. (c) Validation accuracy at each SNN training stage.

Figure 7: Delay search and training of Falcon-Medium on SSC. (a) Validation accuracy and latency of Fastest, Balanced, and Accurate before SNN training. (b) Comparison with ten random delay schedules at the same core latency before SNN training. (c) Validation accuracy at each SNN training stage.

Figure 8: Validation accuracy during 30 epochs of QAT for PDS-selected Balanced schedules: (a) GSC-Medium, (b) GSC-Large, and (c) SSC-Medium. Dashed lines mark the ten-epoch budget, and highlighted points indicate the highest validation accuracy within 30 epochs.

Schedule	GSC Falcon-Medium	GSC Falcon-Large	SSC Falcon-Medium
PDS	96.31	96.19	83.02
Random 1	96.08	96.18	82.08
Random 2	96.33	96.02	82.39
Random 3	96.25	95.78	82.41
Random 4	96.24	95.81	81.66
Random 5	96.13	96.00	82.01

Appendix

Table 4: Test accuracy (%) after full adaptation with 30 epochs of QAT, 15 epochs of neuron-parameter tuning, and 10 epochs of joint training. Each random schedule matches the delay distribution and modeled core latency of the corresponding PDS Balanced schedule. All runs use the same training seed, and checkpoints are selected by validation accuracy.

Figure 9: Equal-budget QAT comparison on SSC with FALCON-Medium. The PDS-selected Balanced schedule and five random schedules share the same delay distribution and modeled core latency. (a) Validation accuracy over all ten training epochs. (b) A closer view of epochs 4–10.

Figure 10: Equal-budget QAT comparison on GSC with FALCON-Medium. The PDS-selected Balanced schedule and five random schedules share the same delay distribution and modeled core latency. (a) Validation accuracy over all ten training epochs. (b) A closer view of epochs 4–10.

Figure 11: Equal-budget QAT comparison on GSC with Falcon-Large. The PDS-selected Balanced schedule and five random schedules share the same delay distribution and modeled core latency. (a) Validation accuracy over all ten training epochs. (b) A closer view of epochs 4–10.

Figure 12: Analog energy breakdown.

Figure 13: Digital energy breakdown. Core energy includes array arithmetic, internal registers, control, and leakage during the evaluation windows.

Q/K prefix $p$	Core latency ( $\mu$ s)	Before val. (%)	After val. (%)	Before test (%)	After test (%)
7 (reference)	119.64	96.4332	–	96.3108	–
6	114.00	95.9523	96.2930	96.1654	96.3289
5	108.36	94.3192	96.1527	94.4480	96.1290
4	102.72	85.0215	95.8120	84.6524	95.9200
3	97.08	72.1872	91.6942	71.0404	91.1495

Appendix

Table 5: Q/K prefix ablation on GSC with Falcon-Medium.

Component	Falcon-Medium	Falcon-Large
Input-encoder convolutions	1	1
Pipelined-stem convolutions	2	2
Pipelined-stem FC layers	2	2
Transformer blocks	3	5
Hidden dimension	160	160
Attention heads	5	5

Appendix

Table 6: Architecture summary of the two Falcon variants. FC counts include the separate Q/K/V and output projections in every attention module, both feed-forward layers, the two stem FC layers, and the classifier.

Model	Schedule	Boundaries with $\delta=2$	Boundaries with $\delta=3$
GSC-Medium	Fastest	—	—
	Balanced	Block 0: FFN1; Block 2: AttnOut	Block 2: Residual2
	Accurate	Stem Conv2	Blocks 0 and 1: FFN1; Block 2: AttnOut and Residual2
GSC-Large	Fastest	—	—
	Balanced	Block 2: Residual1; Block 3: FFN1	Block 4: Residual2
	Accurate	Blocks 0, 1, 3, and 4: FFN1; Block 2: Residual1; Blocks 3 and 4: AttnOut	Stem Conv2; Block 2: FFN1; Block 4: Residual2

Appendix

Table 7: Layer-wise firing delays. Only searchable boundaries with

\delta=2

\delta=3

are listed; all other searchable boundaries use

\delta=1

. A dash indicates that no searchable boundary uses the corresponding delay. Transformer blocks are zero-indexed.

Figure 14: Latency Example of Falcon-Medium with balanced schedule

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School of Software and Microelectronics, Peking University, Beijing, China · School of Computer Science, Peking University, Beijing, China · Institute for Artificial Intelligence and the School of Computer Science, Peking University, Beijing, China

Spiking Neural Networks Time-To-First-Spike Neuromorphic Computing Neural Network Structural Constraints

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Spiking neural networks (SNNs) offer a biologically inspired computing paradigm with significant potential for energy-efficient neural processing. Among neural coding schemes of SNNs, Time-To-First-Spike (TTFS) coding, which encodes information through the precise timing of a neuron's first spike, provides exceptional activity sparsity and energy efficiency. However, existing TTFS models lack efficient training methods, suffering from high inference latency and limited performance, limiting their practicality on neuromorphic hardware. In this work, we propose latency coding, an extension of TTFS coding, and present a compatible framework that enables the efficient training of deep latency-coded SNNs by leveraging backpropagation through time (BPTT) algorithm. The framework includes: (1) a latency encoding (LE) module with feature extraction and straight-through estimators to address severe information loss in direct intensity-to-latency mapping; (2) relaxation of the strict single-spike constraint in intermediate layers to improve information propagation and gradient flow; and (3) a temporal adaptive decision (TAD) loss function that dynamically weights supervision signals based on the model's confidence, balancing the trade-off between speed and accuracy. Experimental results demonstrate that our method achieves competitive or superior accuracy compared with existing TTFS-coded SNNs with ultra-low inference latency and high energy efficiency. Latency-coded SNNs also demonstrate improved robustness against input perturbations. These findings highlight latency coding as a practical and hardware-friendly approach for fast and energy-efficient neuromorphic processing.

Abstract

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