very significant. Enabling On-Device Deployment With the advent of smartphones, Internet-of-Things (IoT) devices (refer to Figure 1-5 for the trend), and the applications deployed on them have to be realtime for data breaches. The law went into effect in 2018. Figure 1-5: Growth in the number of mobile and IoT devices over time. The lighter blue bars represent forecasts. (Data Source: 1, 2) In this book, we well as other computer bots in games like chess, shogi, and go. For the purpose of deployment in IoT and edge devices, both Google and NVidia have come up with accelerators that can be used for fast inference

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mentioned in Chapter 1, TFLite (Tensorflow Lite) helps to convert and deploy tensorflow models to IoT and edge devices. It is optimized for ARM based processors and supports accelerated inference using

memory, this issue can be a bottleneck if the model is going to be deployed on-device (smartphones, IoT devices, etc.), where transmitting the model to the device is limited by the user’s bandwidth, and