Edge computing plays a key role in providing ser-vices for emerging compute-intensive applications while bringing computation close to end devices. FPGAs have been deployed to provide custom acceleration services due to their reconfigurability and support for multi-tenancy in sharing the computing resource. This paper explores an FPGA-based Multi-Accelerator Edge Computing System, that serves various DNN applications from multiple end devices simultaneously. To dynamically maximize the responsiveness to end devices, we propose a system framework that exploits the characteristic of applications in patterns and employs a staggering module coupled with a mixed offline/online multi-queue scheduling method to alleviate resource contention, and uncertain delay caused by network delay variation. Our evaluation shows the framework can significantly improve responsiveness and robustness in serving multiple end devices.

In this paper, we present a method to find an optimal trade-off between computation and communication of decentralized linear task chain running on a network of mobile agents. Task replication has been deployed to reduce the data links among highly correlated nodes in communication networks. The primary goal is to reduce or remove the data links at the cost of increase in computational load at each node. However, with increase in complexity of applications and computation load on end devices with limited resources, the computational load is not negligible. Our proposed selective task replication enables communication-computation trade-off in decentralized task chains and minimizes the overall local computation overhead while keeping the critical path delay under a threshold delay. We applied our approach to decentralized Unscented Kalman Filter (UKF) for state estimation in cooperative localization of mobile multi-robot systems. We demonstrate and evaluate our proposed method on a network of 15 Raspberry Pi3B connected via WiFi. Our experimental results show that, using the proposed method, the prediction step of decentralized UKF is faster by 15%, and for the same threshold delay, the overall computation overhead is reduced by 2.41 times, compared to task replication without resource constraint.

Estimation techniques such as Unscented Kalman filter (UKF) are deployed for accurate joint location estimation in cooperative localization of cyber physical systems (CPS), e.g., to locate each robot in a cooperative mobile robotic network in GPS-denied environments. In order to avoid single point of failure in the centralized implementation of such estimation techniques, the decentralization of estimation algorithms has attracted considerable attention in the past two decades. However, the design of decentralized algorithms with reduced communication cost without loss in accuracy for compute-intensive estimation techniques such as UKF has been challenging. In the decentralized UKF, the tasks are partitioned and computed locally at robot nodes. Data communication overhead is overwhelming due to tight data dependency between the robots' computations. In this paper, we present a CPS framework for UKF decentralization in which computation and communication are tightly intertwined and computation replication is deployed in order to reduce the communication overhead among cooperative mobile robots. We demonstrate and evaluate the performance of our proposed work in a wireless network of 15 Raspberry Pi 3 B, with quad-core 1.2GHz 64bit CPU, emulating a network of mobile robots with onboard computation and communication capabilities. Our experimental results show that the End-to-End execution time of decentralized UKF prediction and update steps with replication are faster by up to 12.29 and 3.57 times, respectively, compared to the partially decentralized UKF algorithm of [1].

The size of configuration bitstreams of field-programmable gate arrays (FPGA) is increasing rapidly. Compression techniques are used to decrease the size of bitstreams. In this paper, an appropriate bitstream format and variable symbol lengths are proposed to utilize the routing patterns for enhancing the compression efficiency. An order of inputs of multiplexers in switch modules is also proposed to improve the symbol statistics and hence, the compression efficiency. A framework to generate the bitstream and hardware description of FPGAs is developed as well. Experimental results over 20 MCNC benchmarks show that by applying the proposed approaches, the compression rate is improved by 46% on average compared to the methods with fixed symbol lengths without any area and performance degradation.

In recent years, parameter variations present critical challenges for manufacturability and yield on integrated circuits. In this paper, a new method for improving the timing yield of field programmable gate array (FPGA) devices affected by random and systematic within-die variation is proposed. By selection of an appropriate configuration from a set of functionally equivalent configurations average critical path delay is reduced under conditions of large random and systematic variation considering spatial correlation. Compared to the previous approach which is limited to a fixed placement, our method improves timing yield by attempting several placements and routings without lengthy placement and routing phases to handle systematic variations and spatial correlation. The average critical path delay is reduced by 7% compared to the previous work over 20 MCNC benchmarks. General Terms: FPGA, process variation, placement, routing