We present GRIP, a graph neural network accelerator architecture designed for low-latency inference. Accelerating GNNs is challenging because they combine two distinct types of computation: arithmetic-intensivevertex-centricoperations and memory-intensiveedge-centricoperations. GRIP splits GNN inference into a three edge- and vertex-centric execution phases that can be implemented in hardware. GRIP specializes each unit for the unique computational structure found in each phase. For vertex-centric phases, GRIP uses a high performance matrix multiply engine coupled with a dedicated memory subsystem for weights to improve reuse. For edge-centric phases, GRIP use multiple parallel prefetch and reduction engines to alleviate the irregularity in memory accesses. Finally, GRIP supports several GNN optimizations, including an optimization called vertex-tiling that increases the reuse of weight data. We evaluate GRIP by performing synthesis and place and route for a$28 \;\mathrm{n}\mathrm{m}$implementation capable of executing inference for several widely-used GNN models (GCN, GraphSAGE, G-GCN, and GIN). Across several benchmark graphs, it reduces 99th percentile latency by a geometric mean of$17\times$and$23\times$compared to a CPU and GPU baseline, respectively, while drawing only$5 \;\mathrm{W}$.

Internet of Things (IoT) devices, once deployed, must remain secure for their entire lifetime, which can be as long as 20 years. Over this lifetime, devices must be able to update which ciphers they use to meet evolving security requirements. However, devices cannot rely on software updates for their cryptography because software implementations consume too much energy. At the same time, fixed function hardware accelerators such as an AES engine cannot support new ciphers. This paper presents Falcon, a hardware architecture for accelerating a broad range of cryptography on energy limited devices. Rather than accelerate a fixed set of current ciphers, Falcon provides a general execution engine that accelerates dominant and emerging ciphers, such as AES, Cha-Cha, SHA-256, RSA, ECC with Curve25519, as well as post-quantum ciphers such as R-LWE. For cryptography, Falcon provides the flexibility of software while reducing the energy consumption of cryptography by 5-60x compared to software. This reduction makes it feasible for IoT applications to upgrade the ciphers they use after deployment, allowing them to keep up to date with security best practices without reducing their deployment lifetime or reducing the application workload. In an application monitoring the temperature of sensitive medical supplies in hospitals, Falcon doubles the deployment lifetime (2.2x).

We describe Tethys, an energy-harvesting wireless water flow sensor that can monitor water use at a per-fixture level with the intention of associating water use with specific individuals. Tethys was motivated by recent efforts at Stanford University to reduce water use due to the California drought. Understanding how the university population uses water at per-person level can greatly influence policies and conservation approaches. Tethys uses Bluetooth Smart to both identify individuals as well as asynchronously upload data to the cloud for later analysis. We describe two challenges encountered in deploying Tethys: energy harvesting design and the mechanical considerations for high-pressure water at high temperatures.