Our GAP8 application processor chip is great at analyzing and understanding data from IoT sensors, from the simplest to the most complex, in a very tight power envelope – from a few tens of milliwatt in active mode down to a few microwatts in sleep mode.
To evaluate the performance that can be expected from our GAP8 chip, we offer GAPuino, an evaluation board compatible with the Arduino ecosystem. This is a generic board that runs off some external power supply or USB and, depending on evaluation intent, can be augmented by additional « shields » (in Arduino terminology) bearing e.g sensors or communication modules.
To go beyond the generic evaluation enabled by GAPuino, we have also developed the « GAPPoc » concept. GAPPoc stands for GAP8 Proof of Concept. The idea here is to focus on a specific class of applications and embed on a single, compact board everything that is needed to demonstrate the target application: not only the GAP8 chip and closely coupled hardware such as crystal or external memory, but also sensors and radio fit for that particular class of applications, as well as a battery.
GAPPoc boards are intended to be used as credible demonstrators representative of a final application in terms of features, performance, power profile and battery life as well as, up to a certain point, physical dimensions.
GAPPoc Architecture and Implementation Choices
All GAPPoc are architectured around a GAPMod core module, which itself essentially consists of a GAP8 chip and external memory (Flash+RAM) plus a low-power crystal oscillator and power management functions. The GAPMod core module (visible at the center of Fig.1) is assembled on GAPPoc like any other SMD device and is complemented with functionalities in line with the target application (especially sensor and radio) as well as generation of all power supplies from the on-board battery, plus various expansion/monitoring features.
With this approach, the « invariant » part of a GAP8-centric system, which also happens in many cases to be the most critical in terms of PCB design (with memory interfaces and sensitive clock generation hardware) is concentrated on the pre-built GAPMod core module, which can be reused across different variants of GAPPoc targetting different classes of applications.
In the same spirit, other implementation choices are made to keep design of GAPPoc boards relatively simple and cost-efficient for prototype volumes (for instance, using pre-certified radio modules rather than custom designs on PCB). Also, despite a specific GAPPoc targetting a very specific class of applications, we nevertheless strive to keep some flexibility – for example, in the case of visual scene analysis POC, we use interchangeable camera lens (therefore enabling different fields of view) on top of a rather capable image sensor.
Going from a POC running adequate software to a final product design therefore requires some optimizations efforts ; nevertheless GAPPoc is a good starting point to save time.
The first variant of GAPPoc, available today, targets Computer Vision in the visible spectrum.
Upcoming variants are in the works or under the definition, with target applications such as Scene Analysis in the near-IR and in the thermal IR spectrum, Sound Classification, Gesture Recognition or Predictive Maintenance.
Incidentally, if you are a provider of sensors or other IoT-related devices which, like GAP8, bring decisive benefits at ultra-low power; we would like to hear from you and explore the opportunity to develop a proof of concepts including your device.
First family member: GAPPoc-A, Computer Vision Concept Board
The first variant of GAPPoc (pictured in Fig. 1) focuses on low-power computer vision applications.
At its heart is a GAPMod core module integrating a GAP8 chip and a generously sized external memory, with 512Kbit of Flash and 64Mbit of RAM (complementing the internal 4Mbit RAM) on a Hyperbus delivering up to 1.2Mbit/s, to support even demanding classification algorithms.
It is complemented with a XVGA (752×480), 1/3’’ monochrome sensor (with binning/down-sampling options) with global shutter and optional wide dynamic range support – ON Semiconductor’s MT9V034. It comes with an S-mount into which an interchangeable standard M12 lens can be screwed. In a low power scenario, the sensor would typically be operated in snapshot mode and completely turned off between snapshots. The GAPPoc power supply generation scheme is designed to allow this. The objective is to obtain battery life greater than 1 year when taking in average one picture every 30 seconds at least.
The radio link is provided by a Bluetooth Low Energy (BLE) module from uBlox (NINA-B112, itself based on the nRF52832 SOC from Nordic), with a very compact on-board antenna. This module comes with a pre-flashed firmware providing a serial link over BLE based on a proprietary BLE profile, as well as the possibility to define custom GATT profiles. This is controlled through AT commands over a UART interface. A third possible option is to completely rewrite Nina’s firmware, making use of Nordic’s SDK.
GAPPoc also offers an ultra-low profile Hirose DF40 connector bearing a selection of power supplies and interface signals (eg. I2C, SPI, GPIO, I2S…) , which allows to securely clip a small satellite board to provide additional features. This could for example be a PIR, Time-of-Flight or ambient light sensors, microphones, etc.
An open-source software Board Support Package with example code is also provided.
With our GAP8 IoT Application Processor chip, you can analyze and classify all kinds of data just next to the sensors that produce them, at ultra-low power and without the privacy concerns of uploading this data to the cloud.
If you have in mind a product for which GAP8 would be a good fit and you would like to quickly come up with a Proof Of Concept representative of what your final product could offer, then the GAPPoc concept may help you.
The first member of the GAPPoc family targets understanding contents of visual scenes. Other variants are being added, targetting applications such as scene analysis, gesture recognition, audio classification or predictive maintenance.
We are keen to work with partners on the evolution of our GAPPoc family, so please get in touch with us if you have interests in this domain.