An Experimental Study of Reduced-Voltage Operation in Modern FPGAs for Neural Network Acceleration

We empirically evaluate an undervolting technique, i.e., underscaling the circuit supply voltage below the nominal level, to improve the power-efficiency of Convolutional Neural Network (CNN) accelerators mapped to Field Programmable Gate Arrays (FPGAs). Undervolting below a safe voltage level can lead to timing faults due to excessive circuit latency increase. We evaluate the reliability-power trade-off for such accelerators. Specifically, we experimentally study the reduced-voltage operation of multiple components of real FPGAs, characterize the corresponding reliability behavior of CNN accelerators, propose techniques to minimize the drawbacks of reduced-voltage operation, and combine undervolting with architectural CNN optimization techniques, i.e., quantization and pruning. We investigate the effect of environmental temperature on the reliability-power trade-off of such accelerators. We perform experiments on three identical samples of modern Xilinx ZCU102 FPGA platforms with five state-of-the-art image classification CNN benchmarks. This approach allows us to study the effects of our undervolting technique for both software and hardware variability. We achieve more than 3X power-efficiency (GOPs/W) gain via undervolting. 2.6X of this gain is the result of eliminating the voltage guardband region, i.e., the safe voltage region below the nominal level that is set by FPGA vendor to ensure correct functionality in worst-case environmental and circuit conditions. 43% of the power-efficiency gain is due to further undervolting below the guardband, which comes at the cost of accuracy loss in the CNN accelerator. We evaluate an effective frequency underscaling technique that prevents this accuracy loss, and find that it reduces the power-efficiency gain from 43% to 25%.Comment: To appear at the DSN 2020 conferenc

Using System-on-a-Programmable-Chip Technology to Design Embedded Systems

This paper describes the tools, techniques, and devices used to design embedded products with system–on-a-chip (SoC) type solutions using a large Field Programmable Gate Array (FPGA) with an internal processor core. This new FPGA-based approach is called system-on-a-programmable-chip (SoPC ). The performance tradeoffs present in SoPC systems is compared to more traditional design approaches. Commercial devices, processor cores, and CAD tool flows are described. The issues in SoPC hardware/software design tradeoffs are examined and three example SoPC designs are presented as case studies

Cognitive Radio for Emergency Networks

In the scope of the Adaptive Ad-hoc Freeband (AAF) project, an emergency network built on top of Cognitive Radio is proposed to alleviate the spectrum shortage problem which is the major limitation for emergency networks. Cognitive Radio has been proposed as a promising technology to solve todayâ?~B??~D?s spectrum scarcity problem by allowing a secondary user in the non-used parts of the spectrum that aactully are assigned to primary services. Cognitive Radio has to work in different frequency bands and various wireless channels and supports multimedia services. A heterogenous reconfigurable System-on-Chip (SoC) architecture is proposed to enable the evolution from the traditional software defined radio to Cognitive Radio

Dynamic Scheduling, Allocation, and Compaction Scheme for Real-Time Tasks on FPGAs

Run-time reconfiguration (RTR) is a method of computing on reconfigurable logic, typically FPGAs, changing hardware configurations from phase to phase of a computation at run-time. Recent research has expanded from a focus on a single application at a time to encompass a view of the reconfigurable logic as a resource shared among multiple applications or users. In real-time system design, task deadlines play an important role. Real-time multi-tasking systems not only need to support sharing of the resources in space, but also need to guarantee execution of the tasks. At the operating system level, sharing logic gates, wires, and I/O pins among multiple tasks needs to be managed. From the high level standpoint, access to the resources needs to be scheduled according to task deadlines. This thesis describes a task allocator for scheduling, placing, and compacting tasks on a shared FPGA under real-time constraints. Our consideration of task deadlines is novel in the setting of handling multiple simultaneous tasks in RTR. Software simulations have been conducted to evaluate the performance of the proposed scheme. The results indicate significant improvement by decreasing the number of tasks rejected

VLSI Architectures and Rapid Prototyping Testbeds for Wireless Systems

The rapid evolution of wireless access is creating an ever changing variety of standards for indoor and outdoor environments. The real-time processing demands of wireless data rates in excess of 100 Mbps is a challenging problem for architecture design and verification. In this paper, we consider current trends in VLSI architecture and in rapid prototyping testbeds to evaluate these systems. The key phases in multi-standard system design and prototyping include: Algorithm Mapping to Parallel Architectures – based on the real-time data and sampling rate and the resulting area, time and power complexity; Configurable Mappings and Design Exploration – based on heterogeneous architectures consisting of DSP, programmable application-specific instruction (ASIP) processors, and co-processors; and Verification and Testbed Integration – based on prototype implementation on programmable devices and integration with RF units.Nokia Foundation FellowshipNokia CorporationNational InstrumentsNational Science Foundatio