Multimodal interactions can reduce cognitive load in demanding situations, such as driving [68, 69, 85]. While such interfaces are becoming more mainstream in vehicles, knowledge of situational and contextual factors which influence user preferences about these interactions is sparse. Through semi-structured interviews with 15 individuals, we take a scenario-specific look at perceptions, expectations, and concerns of voice, gesture, and multimodal interactions within vehicles. We presented participants with scenarios where cognitive load, social norms, and other situational factors vary greatly, such as heavy-traffic or multiple passengers. We found situational preferences of voice and gesture shifted greatly, and often contradicted broadly stated preferences. For example, users generally prefer touch, but use voice in complex traffic, and prefer gesture around other occupants to not interrupt conversations. Based on our findings, we share design considerations that can help interaction designers account for variations in user preference when designing modally flexible automotive interfaces.

As voice assistant usage continues to grow, their homogeneity becomes even more problematic with the UNESCO report, “I’d Blush if I could” showing that designing only feminine voice assistants encourages negative behavior, both with virtual assistants and with real people [3]. While masculine text-to-speech (TTS) voices exist, ones that cover the full range of gender presentations, such as non-binary or gender-ambiguous voices are largely missing. In this paper, we present a method of creating a non-binary TTS voice and an example voice, Sam, created with input from the non-binary and transgender communities. We have open-sourced the resulting voice, along with the process and data used to create it. Finally, we present results from a large-scale survey showing that non-binary individuals are more likely to prefer a non-binary voice assistant compared to cisgendered individuals and discuss differences across age and gender.

Today’s machine learning (ML) systems, running workloads, such as deep neural networks, which require billions of parameters and many hours to train a model, consume a significant amount of energy. Due to the complexity of computation and topology, even the quantized models are hard to deploy on edge devices under energy constraints. To combat this, researchers have been focusing on new emerging neuromorphic computing models. Two of those models are hyperdimensional computing (HDC) and spiking neural networks (SNNs), both with their own benefits. HDC has various desirable properties that other ML algorithms lack, such as robustness to noise, simple operations, and high parallelism. SNNs are able to process event-based signal data in an efficient manner. This work develops$\mathsf {HyperSpike}$, which utilizes a single, randomly initialized, and untrained SNN layer as a feature extractor connected to a trained HDC classifier. HDC is used to enable more efficient classification as well as provide robustness to errors. We experimentally show that$\mathsf {HyperSpike}$is on average$31.5\times $more robust to errors than traditional SNNs. On Intel’s Loihi (Davies et al., 2018),$\mathsf {HyperSpike}$is$10\times $faster and$2.6\times $more energy efficient over traditional SNN networks. We further develop$\mathsf {HyperSpikeASIC}$, a customized accelerator for$\mathsf {HyperSpike}$. By decoupling the neuron and synapses,$\mathsf {HyperSpikeASIC}$skips the inactive neurons and limits the neuron state updating to once per time step at most.$\mathsf {HyperSpikeASIC}$is$601\times $faster and$3467\times $more energy efficient than$\mathsf {HyperSpike}$running on Intel’s Loihi for SNN acceleration, and$12.2\times $faster and$211\times $more energy efficient than the state-of-the-art SNN ASIC implementation (Wang et al., 2022).

Today's Machine Learning(ML) systems, especially those running in server farms running workloads such as Deep Neural Networks, which require billions of parameters and many hours to train a model, consume a significant amount of energy. To combat this, researchers have been focusing on new emerging neuromorphic computing models. Two of those models are Hyperdimensional Computing (HDC) and Spiking Neural Networks (SNNs), both with their own benefits. HDC has various desirable properties that other Machine Learning (ML) algorithms lack such as: robustness to noise in the system, simple operations, and high parallelism. SNNs are able to process event based signal data in an efficient manner. In this paper, we create HyperSpike, which utilizes a single, randomly initialized and untrained SNN layer as feature extractor connected to a trained HDC classifier. HDC is used to enable more efficient classification as well as provide robustness to errors. We experimentally show that HyperSpike is on average 31.5× more robust to errors than traditional SNNs. We also implement HyperSpike in hardware, and show that it is 10x faster and 2.6× more energy efficient over traditional SNN networks run on Intel's Loihi [1].