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Time-to-first-spike(TTFS ) encoded spiking neural networks (SNNs), implemented using memristive crossbar arrays (MCA), achieve higher inference speed and energy efficiency compared to artificial neural networks (ANNs) and rate encoded SNNs. However, memristive crossbar arrays are vulnerable to conductance variations in the embedded memristor cells. These degrade the performance of TTFS encoded SNNs, namely their classification accuracy with adverse impact on the yield of manufactured chips. To combat this yield loss, we propose a post-manufacture testing and tuning framework for these SNNs. In the testing phase, a timing encoded signature of the SNN, which is statistically correlated to the SNN performance, is extracted. In the tuning phase, this signature is mapped to optimal values of the tuning knobs (gain parameters), one parameter per layer, using a trained regressor, allowing very fast tuning (about 150ms). To further reduce the tuning overhead, we rank order hidden layer neurons based on their criticality and show that adding gain programmability only to 50% of the neurons is sufficient for performance recovery. Experiments show that the proposed framework can improve yield by up to 34% and average accuracy of memristive SNNs by up to 9%.
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This content will become publicly available on May 1, 2026
Signature Driven Post-Manufacture Testing and Tuning of RRAM Spiking Neural Networks for Yield Recovery
Resistive random access Memory (RRAM) based spiking neural networks (SNN) are becoming increasingly attractive for pervasive energy-efficient classification tasks. However, such networks suffer from degradation of performance (as determined by classification accuracy) due to the effects of process variations on fabricated RRAM devices resulting in loss of manufacturing yield. To address such yield loss, a two-step approach is developed. First, an alternative test framework is used to predict the performance of fabricated RRAM based SNNs using the SNN response to a small subset of images from the test image dataset, called the SNN response signature (to minimize test cost). This diagnoses those SNNs that need to be performance-tuned for yield recovery. Next, SNN tuning is performed by modulating the spiking thresholds of the SNN neurons on a layer-by-layer basis using a trained regressor that maps the SNN response signature to the optimal spiking thresholdvalues during tuning. The optimal spiking threshold values are determined by an off-line optimization algorithm. Experiments show that the proposed framework can reduce the number of out-of-spec SNN devices by up to 54% and improve yield by as much as 8.6%.
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- Award ID(s):
- 2414361
- PAR ID:
- 10586753
- Editor(s):
- IEEE
- Publisher / Repository:
- Proceeding, IEEE/SIGDA Asian South Pacific Design Automation Conference
- Date Published:
- Edition / Version:
- 1
- Volume:
- 1
- Issue:
- 1
- ISSN:
- 2153-697X
- ISBN:
- 979-8-3503-9354-5
- Page Range / eLocation ID:
- 1-6
- Subject(s) / Keyword(s):
- Spiking Neural Network Yield Recovery Alternative Test Post-manufacture Tuning
- Format(s):
- Medium: X Size: 1MB Other: pdf
- Size(s):
- 1MB
- Location:
- Seoul, Korea
- Sponsoring Org:
- National Science Foundation
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