Processors that adapt architecture to workloads at runtime promise compelling performance per watt (PPW) gains, offering one way to mitigate diminishing returns from pipeline scaling. State-of-the-art adaptive CPUs deploy machine learning (ML) models on-chip to optimize hardware by recognizing workload patterns in event counter data. However, despite breakthrough PPW gains, such designs are not yet widely adopted due to the potential for systematic adaptation errors in the field.

We present a compiler for Loihi, a novel manycore neuromorphic processor that features a programmable, on-chip learning engine for training and executing spiking neural networks (SNNs). An SNN is distinguished from other neural networks in that (1) its independent computing units, or "neurons", communicate with others only through spike messages; and (2) each neuron evaluates local learning rules, which are functions of spike arrival and departure timings, to modify its local state. The collective neuronal state dynamics of an SNN form a nonlinear dynamical system that can be cast as an unconventional model of computation. To realize such an SNN on Loihi requires each constituent neuron to locally store and independently update its own spike timing information. However, each Loihi core has limited resources for this purpose and these must be shared by neurons assigned to the same core. In this work, we present a compiler for Loihi that maps the neurons of an SNN onto and across Loihi's cores efficiently. We show that a poor neuron-to-core mapping can incur significant energy costs and address this with a greedy algorithm that compiles SNNs onto Loihi in a power-efficient manner. In so doing, we highlight the need for further development of compilers for this new, emerging class of architectures.

Wireless data transfer under high mobility, as found in unmanned aerial vehicle (UAV) applications, is a challenge due to varying channel quality and extended link outages. We present FlowCode, an easily deployable link-layer solution utilizing multiple transmitters and receivers for the purpose of supporting existing transport protocols such as TCP in these scenarios. By using multiple transmitters and receivers and by exploiting the resulting antenna beam diversity and parallel transmission effects, FlowCode increases throughput and reception range. In emulation, we show that TCP over FlowCode gives greater goodput over a larger portion of the flight path, compared to an enhanced TCP protocol using the standard 802.11 MAC. In the process, we make a strong case for using trace-modulated emulation when developing distributed protocols for complex wireless environments.

We consider the problem of localizing wireless nodes in an outdoor, open-space environment, using ad-hoc radio ranging measurements, e.g., 802.11. We cast these ranging measurements as a set of distance constraints, thus forming an over-determined system of equations suitable for non-linear least squares optimization. However, ranging measurements are often subject to errors, induced by multipath signals and variations in path loss, unreliable hardware or antenna connectors, or imperfection in measurement models. Such potentially large, non-Gaussian errors in the measurement data ultimately produce inaccurate localization solutions. We propose a new error-tolerant localization method, called snap-inducing shaped residuals (SISR), to identify automatically "bad nodes" and "bad links" arising from these errors, so that they receive less weight in the localization process. In particular, SISR snaps "good nodes" to their accurate locations and gives less emphasis to other nodes. While the mathematical techniques used by SISR are similar to robust statistics, SISR's exploitation of the snap-in effect in localization appears to be novel. We provide analysis on the principle of SISR, illustrate errors in real-world measurements, and demonstrate a working SISR implementation in field experiments on a testbed of 37 wireless nodes, as well as show the superior performance of SISR in simulation with a larger number of nodes.