Just a short extract from the article in case others are having trouble getting it to open:
Neuromorphic TinyML
TinyML (tiny machine learning) is all about executing ML and NNs on tightly memory/processor constrained devices such as microcontrollers (MCUs). As a result, it’s a natural step to incorporate a neuromorphic core for TinyML use cases due to several distinct advantages.
Neuromorphic devices are event-based processors operating on non-zero events. Event-based convolution and dot products are significantly less computationally expensive since zeroes aren’t processed.
Event-based convolution performance improves further with the larger number of zeroes in the filter channels or kernels. This along with activation functions such as Relu being centered around zero provides the property of event-based processors’ inherent activation sparsity, thus reducing effective MAC requirements.
Furthermore, as a neuromorphic device’s process spikes, more constrained quantization can be used, such as 1-,2- and 4-bit quantization, versus the conventional 8-bit quantization on ANNs. Moreover, because SNNs are incorporated into hardware, neuromorphic devices (such as Akida from Brainchip) have the unique capability of on-edge learning.
That’s not possible with conventional devices. They only simulate a neural network with Von Neumann architecture, leading to on-edge learning being computationally expensive with large memory overheads in a TinyML systems budget. In addition, to train a NN model, integers would not provide enough range to train a model accurately. Therefore, training with 8 bits isn’t currently feasible on traditional architectures.
For traditional architectures, a few on-edge learning implementations with machine-learning algorithms (autoencoders, decision trees) have reached a production stage for simple real-time analytics use cases, whereas NNs are still under research.
To summarize, the advantages of using neuromorphic devices and SNNs at the endpoint include:
- Ultra-low power consumption (millijoule to microjoule per inference)
- Lower MAC requirements as compared to conventional NNs
- Lower parameter memory usage as compared to conventional NNs
- On-edge learning capabilities
Neuromorphic TinyML Use Cases
Microcontrollers with neuromorphic cores can excel in use cases throughout the industry
(Fig. 3) thanks to their distinct characteristics of on-edge learning, such as:
- In anomaly-detection applications for existing industrial equipment, using the cloud to train a model is inefficient. Adding an endpoint AI device on the motor and training on the edge would allow for ease of scalability, as equipment aging tends to differ from machine to machine even if they’re the same model.
- In robotics, as time passes, the joints of robotic arms tend to wear down, becoming untuned and stop operating as needed. Re-tuning the controller on the edge without human intervention mitigates the need to call a professional, reducing downtime and saving time and money.
- In face-recognition applications, a user would have to add their face to the dataset and retrain the model on the cloud. With a few snaps of a person’s face, the neuromorphic device can identify the end-user via on-edge learning. Thus, users’ data can be secured on the device, and there’s a more seamless experience. This can be employed in cars, where different users have different preferences on seat position, climate control, etc.
- In keyword-spotting applications, extra words can be added to your device to recognize on the edge. It can be used in biometric applications, where a person would add a “secret word” that they would want to keep secure on the device.
Renesas Electronics
3. These represent some edge-computing learning use cases for neuromorphic devices.
The balance of ultra-low-power neuromorphic endpoint devices and enhanced performance makes them suitable for prolonged battery-powered applications, executing algorithms not possible on other low-power devices due to them being computationally constrained
(Fig. 4). Or they can be applied to higher-end devices capable of similar processing power that’s too power-hungry. Use cases include:
- Smartwatches that monitor and process the data at the endpoint, sending only relevant information to the cloud.
- Smart camera sensors for people detection to execute a logical command. For instance, automated door opening when a person is approaching, as current technology is based on proximity sensors.
- Area with no connectivity or charging capabilities, such as in forests for smart animal tracking or monitoring under ocean pipes for any potential cracks using real-time vibration, vision, and sound data.
- For infrastructure monitoring use cases, where a neuromorphic MCU can be used to continuously monitor movements, vibrations, and structural changes in bridges (via images) to identify potential failures.
Renesas Electronics
4. These use cases can be implemented using ultra-low-power solutions with high performance using SNNs.
On this front,
Renesas has acknowledged the vast potential of neuromorphic devices and SNNs. The company licensed a neuromorphic core from
Brainchip,3,4 the world’s first commercial producer of neuromorphic IP.
References
1. “Neuromorphic computing market –industry analysis, size, share, growth, trends, and forecast, 2020-2028,”
sheeranalyticsandinsights.com.
https://www.sheeranalyticsandinsights.com/market-report-research/neuromorphic-computing-market-21/.
2. “
Neuromorphic Chip Market Growth, Forecast (2022-27)” | Industry Trends.
3. “
BrainChip’s Akida set for spaceflight via NASA as Renesas Electronics America signs first IP agreement”.
4. “
ARM battles RISC-V at Renesas,” eeNews Europe.
