A traditional Machine Learning setup includes at least 16 GB of RAM, a powerful processor – preferably Core i7 and above, a powerful GPU and an efficient SSD with at least 500 GB of storage capacity. This kind of heavy system requirement keeps Machine Learning out of bounds for IoT devices. IoT devices work on a microwatt or milliwatt level! So how can they support such a heavy system requirement? This is where Tiny ML comes in. Aceso Analytics helps you set up Tiny ML models on your embedded systems enabling you to take advantage of Machine Learning even if you have a 1000% less system resource.
When IoT meets Machine Learning, magic happens. Machine Learning models in IoT devices unlock the true power of embedded devices, resulting in positive experiences for businesses and individual users.
Since TinyML runs on the edge, you can leverage it to process the data coming from sensors before sending it to the server. We at Aceso Analytics combine the power of microservices with TinyML helping you process data proactively. The result? The size of data sent to the server/cloud is dramatically reduced, ensuring low latency.
TinyML can work flawlessly with just some kilobytes of RAM and storage capacity. This low resource requirement translates to low energy requirements. It can run on battery power for months and even years. This is specifically useful when IoT devices are deployed in harder-to-access areas.
When it comes to IoT, latency is not the only issue. Therefore, we can’t be too generous with bandwidth. TinyML immensely helps with the initial intelligent data processing. For example, in an industry setup, you can leverage TinyML to monitor temperature continuously and only send the data or raise the alarm when the temperature crosses a certain limit. No need to send temperature data every four or five seconds. This saves a lot of bandwidth.
TinyML is a revolutionary way of solving the resource constraint issue when it comes to using Machine Learning in IoT devices. However, it comes with a set of challenges. Aceso Analytics aims to simplify the implementation of TinyML and make the process painless.
TinyML is made keeping low power consumption in mind. However, your implementation can run into trouble if you have a range of IoT devices with various microcontrollers – each having its own pattern of power consumption. This lack of consistency makes implementing TinyML solutions across a range of devices difficult. Aceso Analytics has expertise in building IoT hardware which enables us to benchmark IoT devices. We help you choose the right kind of IoT setup, calibrated after careful observation of the power consumption pattern.
Aceso Analytics is not just a software company. We build bespoke IoT hardware as well. We have expertise in designing IoT devices that are tailor-made for specific tasks. With this expertise, we build IoT devices with a special focus on accommodating the needs of the TinyML installation. The challenge of low memory, heterogeneous hardware and software can prove to be a limiting factor. Our hardware and software expertise in the IoT world enables us to address these issues effectively.
There are millions of IoT devices that are in use today. Do we need to replace these devices in order to enjoy the benefits of TinyML? No! Aceso Analytic deploys TinyML as a self-contained microservice via software updates. So, if your IoT / edge device employs microservices, you can start leveraging TinyML with just a software update from Aceso Analytics.
We at Aceso Analytics build TinyML solutions with security compliance in mind. No user data is sent to the server for analysis. Instead, data processing happens locally on edge. This enables us to adhere to GDPR and other privacy compliance.