  The increasing interest in serverless computation and ubiquitous wireless
networks has led to numerous connected devices in our surroundings. Among such
devices, IoT devices have access to an abundance of raw data, but their
inadequate resources in computing limit their capabilities. Specifically, with
the emergence of deep neural networks (DNNs), not only is the demand for the
computing power of IoT devices increasing but also privacy concerns are pushing
computations towards the edge. To overcome inadequate resources, several
studies have proposed the distribution of work among IoT devices. These
promising methods harvest the aggregated computing power of the idle IoT
devices in an environment. However, since such a distributed system strongly
relies on each device, unstable latencies, and intermittent failures, the
common characteristics of IoT devices and wireless networks, cause high
recovery overheads. To reduce this overhead, we propose a novel robustness
method with a close-to-zero recovery latency for DNN computations. Our solution
never loses a request or spends time recovering from a failure. To do so,
first, we analyze the underlying matrix-matrix computations affected by
distribution. Then, we introduce a new coded distributed computing (CDC) method
that has a constant cost with the increasing number of devices, unlike the
linear cost of modular redundancies. Moreover, our method is applied in the
library level, without requiring extensive changes to the program, while still
ensuring a balanced work assignment during distribution. To illustrate our
method, we perform experiments with distributed systems comprising up to 12
Raspberry Pis.
