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

On the resilience of deep learning for reduced-voltage FPGAs

Deep Neural Networks (DNNs) are inherently computation-intensive and also power-hungry. Hardware accelerators such as Field Programmable Gate Arrays (FPGAs) are a promising solution that can satisfy these requirements for both embedded and High-Performance Computing (HPC) systems. In FPGAs, as well as CPUs and GPUs, aggressive voltage scaling below the nominal level is an effective technique for power dissipation minimization. Unfortunately, bit-flip faults start to appear as the voltage is scaled down closer to the transistor threshold due to timing issues, thus creating a resilience issue.This paper experimentally evaluates the resilience of the training phase of DNNs in the presence of voltage underscaling related faults of FPGAs, especially in on-chip memories. Toward this goal, we have experimentally evaluated the resilience of LeNet-5 and also a specially designed network for CIFAR-10 dataset with different activation functions of Rectified Linear Unit (Relu) and Hyperbolic Tangent (Tanh). We have found that modern FPGAs are robust enough in extremely low-voltage levels and that low-voltage related faults can be automatically masked within the training iterations, so there is no need for costly software-or hardware-oriented fault mitigation techniques like ECC. Approximately 10% more training iterations are needed to fill the gap in the accuracy. This observation is the result of the relatively low rate of undervolting faults, i.e., <0.1%, measured on real FPGA fabrics. We have also increased the fault rate significantly for the LeNet-5 network by randomly generated fault injection campaigns and observed that the training accuracy starts to degrade. When the fault rate increases, the network with Tanh activation function outperforms the one with Relu in terms of accuracy, e.g., when the fault rate is 30% the accuracy difference is 4.92%.The research leading to these results has received funding from the European Unions Horizon 2020 Programme under the LEGaTO Project (www.legato-project.eu), grant agreement n 780681.Peer ReviewedPostprint (author's final draft

MoRS: An approximate fault modelling framework for reduced-voltage SRAMs

On-chip memory (usually based on Static RAMs-SRAMs) are crucial components for various computing devices including heterogeneous devices, e.g, GPUs, FPGAs, ASICs to achieve high performance. Modern workloads such as Deep Neural Networks (DNNs) running on these heterogeneous fabrics are highly dependent on the on-chip memory architecture for efficient acceleration. Hence, improving the energy-efficiency of such memories directly leads to an efficient system. One of the common methods to save energy is undervolting i.e., supply voltage underscaling below the nominal level. Such systems can be safely undervolted without incurring faults down to a certain voltage limit. This safe range is also called voltage guardband. However, reducing voltage below the guardband level without decreasing frequency causes timing-based faults. In this paper, we propose MoRS, a framework that generates the first approximate undervolting fault model using real faults extracted from experimental undervolting studies on SRAMs to build the model. We inject the faults generated by MoRS into the on-chip memory of the DNN accelerator to evaluate the resilience of the system under the test. MoRS has the advantage of simplicity without any need for high-time overhead experiments while being accurate enough in comparison to a fully randomly-generated fault injection approach. We evaluate our experiment in popular DNN workloads by mapping weights to SRAMs and measure the accuracy difference between the output of the MoRS and the real data. Our results show that the maximum difference between real fault data and the output fault model of MoRS is 6.21%, whereas the maximum difference between real data and random fault injection model is 23.2%. In terms of average proximity to the real data, the output of MoRS outperforms the random fault injection approach by 3.21x.This work is partially funded by Open Transprecision Computing (OPRECOM) project, Summer of Code 2020.Peer ReviewedPostprint (author's final draft

Challenges and Opportunities in Near-Threshold DNN Accelerators around Timing Errors

AI evolution is accelerating and Deep Neural Network (DNN) inference accelerators are at the forefront of ad hoc architectures that are evolving to support the immense throughput required for AI computation. However, much more energy efficient design paradigms are inevitable to realize the complete potential of AI evolution and curtail energy consumption. The Near-Threshold Computing (NTC) design paradigm can serve as the best candidate for providing the required energy efficiency. However, NTC operation is plagued with ample performance and reliability concerns arising from the timing errors. In this paper, we dive deep into DNN architecture to uncover some unique challenges and opportunities for operation in the NTC paradigm. By performing rigorous simulations in TPU systolic array, we reveal the severity of timing errors and its impact on inference accuracy at NTC. We analyze various attributes—such as data–delay relationship, delay disparity within arithmetic units, utilization pattern, hardware homogeneity, workload characteristics—and uncover unique localized and global techniques to deal with the timing errors in NTC

MoRS: An approximate fault modelling framework for reduced-voltage SRAMs

On-chip memory (usually based on Static RAMs-SRAMs) are crucial components for various computing devices including heterogeneous devices, e.g, GPUs, FPGAs, ASICs to achieve high performance. Modern workloads such as Deep Neural Networks (DNNs) running on these heterogeneous fabrics are highly dependent on the on-chip memory architecture for efficient acceleration. Hence, improving the energy-efficiency of such memories directly leads to an efficient system. One of the common methods to save energy is undervolting i.e., supply voltage underscaling below the nominal level. Such systems can be safely undervolted without incurring faults down to a certain voltage limit. This safe range is also called voltage guardband. However, reducing voltage below the guardband level without decreasing frequency causes timing-based faults. In this paper, we propose MoRS, a framework that generates the first approximate undervolting fault model using real faults extracted from experimental undervolting studies on SRAMs to build the model. We inject the faults generated by MoRS into the on-chip memory of the DNN accelerator to evaluate the resilience of the system under the test. MoRS has the advantage of simplicity without any need for high-time overhead experiments while being accurate enough in comparison to a fully randomly-generated fault injection approach. We evaluate our experiment in popular DNN workloads by mapping weights to SRAMs and measure the accuracy difference between the output of the MoRS and the real data. Our results show that the maximum difference between real fault data and the output fault model of MoRS is 6.21%, whereas the maximum difference between real data and random fault injection model is 23.2%. In terms of average proximity to the real data, the output of MoRS outperforms the random fault injection approach by 3.21x.This work is partially funded by Open Transprecision Computing (OPRECOM) project, Summer of Code 2020.Peer ReviewedPostprint (author's final draft

ParaDox: Eliminating Voltage Margins via Heterogeneous Fault Tolerance.

Providing reliability is becoming a challenge for chip manufacturers, faced with simultaneously trying to improve miniaturization, performance and energy efficiency. This leads to very large margins on voltage and frequency, designed to avoid errors even in the worst case, along with significant hardware expenditure on eliminating voltage spikes and other forms of transient error, causing considerable inefficiency in power consumption and performance. We flip traditional ideas about reliability and performance around, by exploring the use of error resilience for power and performance gains. ParaMedic is a recent architecture that provides a solution for reliability with low overheads via automatic hardware error recovery. It works by splitting up checking onto many small cores in a heterogeneous multicore system with hardware logging support. However, its design is based on the idea that errors are exceptional. We transform ParaMedic into ParaDox, which shows high performance in both error-intensive and scarce-error scenarios, thus allowing correct execution even when undervolted and overclocked. Evaluation within error-intensive simulation environments confirms the error resilience of ParaDox and the low associated recovery cost. We estimate that compared to a non-resilient system with margins, ParaDox can reduce energy-delay product by 15% through undervolting, while completely recovering from any induced errors

An Automotive Case Study on the Limits of Approximation for Object Detection

The accuracy of camera-based object detection (CBOD) built upon deep learning is often evaluated against the real objects in frames only. However, such simplistic evaluation ignores the fact that many unimportant objects are small, distant, or background, and hence, their misdetections have less impact than those for closer, larger, and foreground objects in domains such as autonomous driving. Moreover, sporadic misdetections are irrelevant since confidence on detections is typically averaged across consecutive frames, and detection devices (e.g. cameras, LiDARs) are often redundant, thus providing fault tolerance. This paper exploits such intrinsic fault tolerance of the CBOD process, and assesses in an automotive case study to what extent CBOD can tolerate approximation coming from multiple sources such as lower precision arithmetic, approximate arithmetic units, and even random faults due to, for instance, low voltage operation. We show that the accuracy impact of those sources of approximation is within 1% of the baseline even when considering the three approximate domains simultaneously, and hence, multiple sources of approximation can be exploited to build highly efficient accelerators for CBOD in cars