Distributed big data in scientific computing presents a major I/O performance bottleneck when exploiting data paral-lelism. Consumer and producer compute nodes are often throttled by saturated data channels when processing large numerical data. We describe ZHW, a hardware implementation of the ZFP numerical CODEC that can greatly reduce I/O pressure caused by large scientific datasets. Our ZHW design overcomes barriers that have prevented prior ZFP-like hardware accelerators from obtaining maximum compression in their implementations. The SystemC ZHW hardware library is available in an open source public repository. We demonstrate the practicality of ZHW by synthesizing our CODEC on an Ultrascale+ FPGA and analyzing performance.

Batteryless sensors avoid battery replacement at the cost of slowing down or stopping their operations when there is not sufficient energy to harvest in the environment. While this strategy can work for some applications, event-based applications still remain a challenge as events arrive sporadically and energy availability is uncertain. One solution is to only turn On a sensor right before an event is happening to both detect the event and save as much energy as possible. Therefore, the system has to correctly predict events while managing limited resource availability. In this demo, we present Ember, an energy management system based on deep reinforcement learning to duty cycle event-driven sensors in low-energy conditions. We show how our system learns environmental patterns over time and makes decisions to maximize the event detection rate for batteryless energy-harvesting sensor nodes subject to low energy availability. Furthermore, we show a novel self-supervised data collection algorithm that helps Ember in discovering new environmental patterns over time. For more details, we refer readers to the full paper of Ember [2].

Non-rigid 3D registration is a technique for matching 3D scans of a scene involving deformable objects. Augmented reality, gesture recognition, medical imaging, and many other computer vision and graphics applications require real-time registration to model deformable or articulated objects. Unfortunately, non-rigid registration is a computationally intensive problem that requires careful optimization to maximize throughput and latency. We present a FPGA+CPU accelerator for real-time non-rigid 3D registration based on Tree Reweighted Message Passing (TRW-S). We overcome memory bound issues and scheduling limitations of conventional TRW-S by dynamically generating the Markov Random Fields. This, along with a bevy of other architectural optimizations, allows us to almost saturate 1024 multipliers in a Arria 10 at 100MHz. We achieve a 600x speed up over baseline TRW-S and our registration architecture has up to 81x energy reduction over a software implementation of our algorithm. We demonstrate the performance of our system by performing real-time (20 scan per second) registration on a complicated surgical scene.

The RISC-V specification is a highly flexible specification for low-cost processors. The RISC-V ISA is royalty free, vendor agnostic, easily portable between development environments, and highly flexible to match the demands of an application. These characteristics make RISC-V a natural ISA choice for an FPGA soft processor and this has led to widespread adoption in academia and industry. However, the sheer number of RISC-V projects can be daunting for potential users. This paper describes a tool for exploring RISC-V projects. Our tool provides a web-interface for executing C/C++ code, tests, and benchmarks. Our tool is packaged with interactive tutorials for extending, modifying, and reproducing our work.

Research experiences for undergraduates (REUs) have many positive outcomes on students' perception of and retention in Computer Science (CS). Yet nearly all REUs are aimed at late-college students, well into a CS program. We present the Early Research Scholars Program (ERSP), a 4 quarter program designed to engage early-college (first or second year) CS students in high-quality research experiences in active research groups at a large research university. ERSP's structured course-supported group-apprentice model and its unique dual advising structure make it possible to vastly increase number of early-career CS students who participate in high-quality research experiences with little additional burden on individual faculty mentors. ERSP's focus on community building and support makes it particularly appropriate for students from groups who are traditionally underrepresented in CS. This paper reports the structure of the program and observations and learning thus-far with ERSP, with the goal of enabling others to implement this program at other large research-focused universities.