Packet Compression in GPU Architectures

Graphical processing unit (GPU) can support multiple operations in parallel by executing it on multiple thread unit known as warp i.e. multiple threads running the same instruction. Each time miss happens at private cache of Streaming Multiprocessor (SM), the request is migrated over the network to shared L2 cache and then later down to Memory Controller (MC) for supplying memory block. The interconnect delay becomes a bottleneck due to a large number of requests from different SM and multiple replies from the MCs. The compression technique can be used to mitigate the performance bottleneck caused by a large volume of data. In this work, I apply various compression algorithms and propose a new compression scheme, Data Segment Matching (DSM). I apply approximation to the floating-point elements to improve compressibility and develop a prediction model to identify number of approximation bits. I focus on compression techniques to resolve this bottleneck. The evaluations using a cycle accurate simulator show that this scheme improves Instructions per Cycle (IPC) by 12% on an average across various benchmarks with compressibility 50% in integer type benchmarks and 35% in floating-point type benchmarks when the proposed scheme is applied to packet compression in the interconnection network

Contextual Proactive Suggestions for Custom Commands for Vehicle Components

Many vehicle components that are sourced from OEMs offer interactive capabilities that enable users to control them using mechanisms such as a voice-based virtual assistant via a spoken command. However, users typically lack awareness of the existence of the commands, thus failing to use them despite their availability. Mechanisms for facilitating proactive and contextually opportune discovery of interactive control options are typically limited to first party components. This disclosure describes techniques that enable OEMs to facilitate user discovery of their custom interactive capabilities by registering the commands and associated metadata with the vehicle platform. With user permission, local contextual information is analyzed to provide suggestions for OEM commands at opportune times based on triggering rules. OEMs can contribute to defining the triggering rules. The user can issue the command in any convenient manner, such as tapping or speaking which is then executed on the OEM component. The proactive and contextually relevant assistance can make the driving experience safer and more comfortable while enhancing the knowledge of vehicle capabilities and how they can be controlled through a virtual assistant