You've probably heard that statistic that every search on ChatGPT uses the equivalent of a bottle of water. And while that's technically true, it misses some of the nuance. The MIT Technology Review dropped a massive report that reveals how the artificial intelligence industry uses energy -- and exactly how much energy it costs to use a service like ChatGPT. The report determined that the energy cost of large-language models like ChatGPT cost anywhere from 114 joules per response to 6,706 joules per response -- that's the difference between running a microwave for one-tenth of a second to running a microwave for eight seconds. The lower-energy models, according to the report, use less energy because they uses fewer parameters, which also means the answers tend to be less accurate.

Deep learning models are computationally intense, and implementations often have to be highly optimized by experts or hardware vendors to be usable in practice. The DL compiler, together with Learning-to-Compile has proven to be a powerful technique for optimizing tensor programs. However, a limitation of this approach is that it still suffers from unbearably long overall optimization time. In this paper, we present a new method, called AdaTune, that significantly reduces the optimization time of tensor programs for high-performance deep learning inference. In particular, we propose an adaptive evaluation method that statistically early terminates a costly hardware measurement without losing much accuracy. We further devise a surrogate model with uncertainty quantification that allows the optimization to adapt to hardware and model heterogeneity better. Finally, we introduce a contextual optimizer that provides adaptive control of the exploration and exploitation to improve the transformation space searching effectiveness. We evaluate and compare the levels of optimization obtained by AutoTVM, a stateof-the-art Learning-to-Compile technique on top of TVM, and AdaTune. The experiment results show that AdaTune obtains up to 115% higher GFLOPS than the baseline under the same optimization time budget.

Learning neural radiance fields of a scene has recently allowed realistic novel view synthesis of the scene, but they are limited to synthesize images under the original fixed lighting condition. Therefore, they are not flexible for the eagerly desired tasks like relighting, scene editing and scene composition. To tackle this problem, several recent methods propose to disentangle reflectance and illumination from the radiance field. These methods can cope with solid objects with opaque surfaces but participating media are neglected. Also, they take into account only direct illumination or at most one-bounce indirect illumination, thus suffer from energy loss due to ignoring the high-order indirect illumination. We propose to learn neural representations for participating media with a complete simulation of global illumination. We estimate direct illumination via ray tracing and compute indirect illumination with spherical harmonics. Our approach avoids computing the lengthy indirect bounces and does not suffer from energy loss. Our experiments on multiple scenes show that our approach achieves superior visual quality and numerical performance compared to state-of-the-art methods, and it can generalize to deal with solid objects with opaque surfaces as well.

This work brings together three active research areas: variational inference, variational Kalman filtering, and gradient flows on the Wasserstein space.

According to the Figure 6 of the main manuscript, in the LIF model, the intra-neuron dependencies is caused by the firing-and-resetting mechanism. The experimented SNNs are based on the LIF model described in (4) of the main manuscript. The simulation step size is set to 1 ms. Only a few time steps are used to demonstrate low-latency spiking neural computation. The parameters like thresholds and learning rates are empirically tuned.

M-Step only reconstructions with known sources.............. 9 2.5.3 Similar works that use expectation maximization (EM) based deep learning approaches are usually specific to a single task, often times image classification. Results shown are simulated using 20 surface receivers and a varying number of sources (9, 25, and 49) in a uniform grid. The velocity reconstruction MSE is included in the top right of each reconstruction. The model with the highest data likelihood is highlighted in orange.

Below we provide further examples of specifications that can be captured by our framework. Another desirable specification towards ensuring reliable uncertainty calibration for NNs is that the expected uncertainty in the predictions increases monotonically with an increase in the variance of the input-noise distribution. We can capture this specification within the formulation described by equation 1, by letting: 1. P A natural generalization of this specification is one where low reconstruction error is guaranteed in expectation, since in practice the latent-representations that are fed into the decoder are drawn from a normal distribution whose mean and variance are predicted by the encoder. A more general specification is one where we wish to verify that for a set of norm-bounded points around a given input, the expected reconstruction error from the VAE is small. Writing this in terms of expected values, we obtain g (λ).