Second part of the article.
Intel Neuromorphic Platform
This article focuses on Intel’s latest Loihi 2 chip with their new deep-learning framework, Lava Deep Learning (Lava DL) [7]. Zahm et al. [1] utilized the Loihi 1 architecture combined with the SNN-Toolbox, a software package intended for direct ANN-SNN conversion. The Lava DL software package contains two main modules for training networks compatible with Loihi hardware—Spike Layer Error Reassignment in Time (SLAYER) and Bootstrap. SLAYER is intended for native training of deep event-based networks, and Bootstrap is intended for training rate-coded SNNs.
The Bootstrap module of the Lava-DL accelerates the training of SNNs and closes the performance gap compared to an equivalent ANN. SNNs have extended training times compared to ANNs. This method leverages the similarity between the behavior of the leaky integrate and fire (LIF) neuron and the rectified linear unit activation function to produce a piecewise mapping of the former to the latter. This method of ANN-SNN syncing in training is particularly beneficial because it accelerates the training of a rate-coded SNN, reduces the inference latency of the trained SNN, and closes the gap between ANN and SNN accuracy. The network was trained on either a CPU or GPU, and then inference was performed on the Loihi 2 hardware. Testing on an identically structured network to the Akida hardware tests was performed to compare performance between the two product offerings.
The current-based LIF (CUBA) neuron model, combined with an Adam optimizer, and categorical cross entropy loss were used to train the model. The neuron threshold parameter of the LIF neuron affected performance the most of any of the neuron parameters, so values from 0.25 to 1.5 in steps of 0.25 were tested. If the threshold was too low, performance would suffer due to saturation of neuron activation in the subsequent layers. If the threshold was too high, few neurons would activate at all and the performance of the network would suffer. A value of 0.75 provided the best performance. Altering the default values of the other parameters induced erratic neuron behavior and unstable training.
Hyperparameter Tuning
After identifying a sufficient neuron model for the Lava DL model, further hyperparameter optimization was performed for the batch sizes of 256, 512, and 1,024 transactions and learning rates of 1E-3, 1E-4, and 1E-5. Training was performed for 200 epochs for each model to allow convergence for the lowest learning rates.
Results
Full-Precision Neural Network and Hyperparameter Tuning
While all values had an impact on model performance, hidden layer count and batch size were of greatest value for maximizing model performance. Plotting the average of a performance metric for all sweeps grouped by the parameter of interest also shows these relationships. Increased batch size was correlated with decreased model test accuracy (see Figure 2), and increased parameter count via hidden unit design correlated positively with accuracy (not shown). Larger batch sizes sometimes led to more unstable training and decreased accuracy.
Figure 2. Sweep Mean Train (Dotted Line) and Validation Accuracy (Solid Line) for Different Batch Sizes
(Source: Zahm et al.).
By hyperparameter tuning across more than 50 parameter configurations, a model accuracy of 98.42% was achieved on the TON-IoT subset split data with an 80/20 split. This parameter sweep was performed on two NVIDIA A100 nodes and took three hours. Previous hyperparameter sweeps took more time due to larger neural network sizes.
BrainChip Neuromorphic Platform
The following results are presented in this section: an improved data scaling process, an improved ANN to SNN conversion process, and running the converted model on a chip rather than just via software simulator. This gained valuable insights into real hardware inference speeds and power costs.
Data Scaling
The new data scaling method outperformed the old Zahm et al. [1] method on both quantized and converted SNN models, as shown in Table 4. Quantized model performance improved ~3.1% and converted SNN model performance improved ~5.3%. Log-scaling was also tried but did not perform as well as the new method. The reduced accuracy drop was noted when converting the quantized model to an SNN with the data scaled via the new processes (3.9% vs. 1.7%). Passing scaling factors were also introduced to the SNN model in hardware to further reduce this accuracy loss when converting a quantized model. However, this was not used to produce Table 4, as Zahm et al. [1] used this technique. Data scaling experiments were done for SNN training but not ANN training, hence identical ANN performance. Note that identical initial ANN model and identical quantization retraining schedules were used and not the optimal ANN design and optimal quantization retraining schedules.
Table 4. BrainChip, SNN Data Scaling Technique vs. Performance
ANN to SNN Conversion
In Table 5, quantization yields dramatically smaller models to fit on low SWaP-C neuromorphic hardware. Accuracy increased for the ANN and SNN models, and ANN performance increased 4.7%. At 98.4% accuracy, this was similar to the state of the art presented in Gad et al. [2] and Sarhan et al. [3]. Improvements to the quantization schedule reduced the accuracy drop from 11.2% to 7.2% between full and reduced precision models.
Table 5. BrainChip, Accuracy Benchmarks
On-chip Execution
Results for ANN vs. BrainChip SNN size, power, and speed are summarized in Table 6. Power consumption was ~1 W. GPU power was estimated at 30 W, using 10% of an NVIDIA A100’s maximum power consumption. Speed was slower for neuromorphic chips. GPU models could operate with much higher throughput due to batch processing, which might not be available for streaming cybersecurity data.
Table 6. ANN vs. BrainChip SNN Size, Power, and Speed
Intel Neuromorphic Platform
Batch size was negatively correlated with accuracy, while learning rate was positively correlated with accuracy. Larger batch sizes took longer to converge but were less susceptible to random fluctuations in the dataset. The Bootstrap framework appeared to perform better with larger learning rates, whereas ANNs typically preferred smaller learning rates.
A final accuracy of 90.2% was achieved with the Lava DL Bootstrap framework, with an identical architecture to the Akida network, as shown in Table 7. This was a reduction in accuracy of 3.5% compared to the prior work of Zahm et al. [1]. However, the old SNN-Toolbox performed direct ANN-SNN conversion, while Lava DL required implementation and training of a native SNN.
Table 7. Intel Accuracy
A 72.4% reduction in model size was observed between the full-precision ANN and the Lava DL model detailed in Table 8. With over 24 MB of memory available on Loihi 2 chips, this model is expected to comfortably fit on the hardware.
Table 8. ANN vs. Intel Size and Speed
While the Lava DL network could predict normal traffic 99% of the time, it struggled to accurately predict the precise class of non-normal traffic. The highest classification accuracy in non-normal traffic was 52% for DOS attacks.
Discussion
The following was presented from this work: an improved dataset with less normal traffic and improved ANN performance via better data preprocessing and hyperparameter tuning. For BrainChip, accuracy improved, model size decreased, and the model on the Akida chip was assessed for timing and power. Improvements were attributed to better data scaling and rigorous model quantization and retraining. For Intel, the performance of the new Lava DL framework was benchmarked, with a slight dip in performance compared to the prior SNN-Toolbox. However, accuracy was similar to BrainChip. Although the percentage of correct results (~98%) was like the state of the art presented in Gad et al. [2] and Sarhan et al. [3], low neuromorphic processors could be used with dramatic SWaP-C savings (see Table 1). In related work, a semi-supervised approach to cybersecurity on Intel’s Loihi 2 was investigated [8]. Testing these models on Intel hardware and larger and more diverse datasets is a goal for future work.
Conclusions
Because of their low SWaP-C envelope, neuromorphic technologies are well suited for deployable platforms such as manned aircraft or UAVs. Table 1 illustrates the SWaP-C advantages of neuromorphic processors compared to GPUs. Neuromorphic technologies could be used for cybersecurity of embedded networks or other functions like perception or control. Network traffic across CAN buses, for example, could be passed through neuromorphic processors. These processors would then detect abnormal traffic, which could then be blocked, preventing further harm.
Neuromorphic computing was also pursued for computer vision projects. Park et al. [9] used an ANN to SNN conversion to classify contraband materials across a variety of conditions, such as different temperatures, purities, and backgrounds. Neuromorphic technologies for image processing and automatic target recognition were also explored [10]. For image processing, hierarchical attention-oriented, region-based processing (HARP) [11] was used. HARP removes uninteresting image regions to speed up image transfer and subsequent processing. For automatic target recognition, U-net was used to detect tiny targets in infrared images in cluttered, varied scenes. U-net was run on Intel’s Loihi chip [12].
Research and development in cybersecurity and neuromorphic computing continues, with great potential in both.
Acknowledgments
This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research program under Award Number DE-SC0021562.
References
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- Gad, A., A. Nashat, and T. Barkat. “Intrusion Detection System Using Machine Learning for Vehicular Ad Hoc Networks Based on ToN-IoT Dataset.” IEEE Access, vol. 9, pp. 142206–142217, 2021.
- Sarhan, M., S. Layeghy, N. Moustafa, M. Gallagher, and M. Portmann. “Feature Extraction for Machine Learning-Based Intrusion Detection in IoT Networks.” Digital Communications and Networks, vol. 10, pp. 205–216, 2022.
- Moustafa, N. “New Generations of Internet of Things Datasets for Cybersecurity Applications Based on Machine Learning: TON_IoT Datasets.” In Proceedings of the eResearch Australasia Conference, Brisbane, Australia, pp. 21–25, 2019.
- Weights and Biases. “Weights and Biases.” http://www.wandb.com, accessed on 14 April 2023.
- BrainChip. “CNN2SNN Toolkit.” https://doc.brainchipinc.com/user_guide/cnn2snn.html, accessed on 14 April 2023.
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- Bal, M., G. Nishibuchi, S. Chelian, S. Vasan, and A. Sengupta. “Bio-Plausible Hierarchical Semi-Supervised Learning for Intrusion Detection.” In Proceedings of the International Conference on Neuromorphic Systems (ICONS), Santa Fe, NM, 2023.
- Park, K. C., J. Forest, S. Chakraborty, J. T. Daly, S. Chelian, and S. Vasan. “Robust Classification of Contraband Substances Using Longwave Hyperspectral Imaging and Full Precision and Neuromorphic Convolutional Neural Networks.” Procedia Computer Science, vol. 213, pp. 486–495, 2022.
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Biographies
Wyler Zahm is a researcher and senior ML engineer. He has worked with advanced algorithms, front- and back-end development, a variety of AI/ML architectures and frameworks such as full precision/GPU and reduced precision/neuromorphic technologies, and applications like automated vulnerability detection and repair for computer source code and cybersecurity. Mr. Zahn has dual bachelor’s degrees in computer engineering and data science from the University of Michigan.
George Nishibuchi is a researcher in materials science and DL. He has a background in computational materials science, with experience running over 50,000 Density Functional Theory simulations at Purdue University’s Network for Computational Nanotechnology, including phonon studies of infrared transparent ceramics, high-throughput studies of semiconductors, and mechanistic studies in solid-state electrolytes. He has also contributed to research in neuromorphic learning algorithms for network intrusion detection systems. Mr. Nishibuchi has an M.S. in materials engineering from Purdue University.
Aswin Jose is a lead system engineer with more than 12 years of extensive experience in system design, software architecture, and leadership. He possesses a broad and deep expertise in various domains such as generative AI/ML verification and validation (V&V), computer vision, big data analytics, the banking sector, logistics, semiconductor technology, healthcare systems, and cutting-edge technological frameworks like full-stack architectures, lambda architecture, and the MEAN stack. Mr. Jose holds an M.E. in computer science from Anna University.
Suhas Chelian is a researcher and ML engineer. He has captured and executed more than $12 million worth of projects with several organizations like Fujitsu Labs of America, Toyota (Partner Robotics Group), HRL Labs (Hughes Research Lab), DARPA, the Intelligence Advanced Research Projects Agency, and NASA. He has 31 publications and 32 patents demonstrating his expertise in ML, computer vision, and neuroscience. Dr. Chelian holds dual bachelor’s degrees in computer science and cognitive science from the University of California, San Diego and a Ph.D. in computational neuroscience from Boston University.
Srini Vasan is the president and CEO of Quantum Ventura Inc. and CTO of QuantumX, the research and development arm of Quantum Ventura Inc. He specializes in AI/ML, AI V&V, ML quality assurance and rigorous testing, ML performance measurement, and system software engineering and system internals. Mr. Vasan studied management at the MIT Sloan School of Management
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