In this paper, we propose a new dynamic reliability technique
using an accuracy-reconfigurable stochastic computing (ARSC) framework
for deep learning computing. Unlike the conventional stochastic computing
that conducts design time accuracy power/energy trade-off, the new ARSC
design can adjust the bit-width of the data in run time.
Hence, the ARSC can mitigate the long-term aging effects by slowing
the system clock frequency, while maintaining the inference throughput by
reducing the data bit-width at a small cost of accuracy. We show how to
implement the recently proposed counter-based SC multiplication and
bit-width reduction on a layer-wise quantization scheme for CNN networks
with dynamic fixed-point data. We validate an ARSC-based five-layer
convolutional neural network designs for the MNIST dataset based on Vivado
HLS with constraints from Xilinx Zynq-7000 family xc7z045 platform.
Experimental results show that new ARSC DNN can sufficiently compensate
the NBTI induced aging effects in 10 years with marginal classification
accuracy loss while maintaining or even exceeding the pre-aging computing
throughput. At the same time, the proposed ARSC
computing framework also reduces the active power consumption due to the
frequency scaling, which can further improve system reliability due to the
reduced temperature.
more »
« less
Run-time accuracy reconfigurable stochastic computing for dynamic reliability and power management: work-in-progress
In this paper, we propose a novel accuracy-reconfigurable stochastic
computing (ARSC) framework for dynamic reliability and power
management. Different than the existing stochastic computing works,
where the accuracy versus power/energy trade-off is carried out in
the design time, the new ARSC design can change accuracy or
bit-width of the data in the run-time so that it can accommodate the
long-term aging effects by slowing the system clock frequency at
the cost of accuracy while maintaining the throughput of the
computing. We validate the ARSC concept on a discrete cosine
transformation (DCT) and inverse DCT designs for image
compressing/decompressing applications, which are implemented on
Xilinx Spartan-6 family XC6SLX45 platform. Experimental results show that the new design can easily mitigate the
long-term aging-induced effects by accuracy trade-off while
maintaining the throughput of the whole computing process using simple
frequency scaling. We further show that one-bit precision loss for the
input data, which translated to 3.44dB of the accuracy loss in term
of Peak Signal to Noise Ratio (PSNR) for images, we can sufficiently
compensate the NBTI induced aging effects in 10 years while maintaining
the pre-aging computing throughput of 7.19 frames per second. At the same
time, we can save 74\% power consumption by 10.67dB of accuracy loss.
The proposed ARSC computing
framework also allows much aggressive frequency scaling, which can lead to
order of magnitude power savings compared to the traditional dynamic
voltage and frequency scaling (DVFS) techniques.
more »
« less
- NSF-PAR ID:
- 10279539
- Date Published:
- Journal Name:
- Proc. International Conference on Compilers, Architecture, and Synthesis for Embedded Systems (CASES’20), ESWEEK 2020
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
In this work, we present a novel approach to real-time tracking of full-chip heatmaps for commercial off-the-shelf microprocessors based on machine-learning. The proposed post-silicon approach, named RealMaps, only uses the existing embedded temperature sensors and workload-independent utilization information, which are available in real-time. Moreover, RealMaps does not require any knowledge of the proprietary design details or manufacturing process-specific information of the chip. Consequently, the methods presented in this work can be implemented by either the original chip manufacturer or a third party alike, and is aimed at supplementing, rather than substituting, the temperature data sensed from the existing embedded sensors. The new approach starts with offline acquisition of accurate spatial and temporal heatmaps using an infrared thermal imaging setup while nominal working conditions are maintained on the chip. To build the dynamic thermal model, a temporal-aware long-short-term-memory (LSTM) neutral network is trained with system-level features such as chip frequency, instruction counts, and other high-level performance metrics as inputs. Instead of a pixel-wise heatmap estimation, we perform 2D spatial discrete cosine transformation (DCT) on the heatmaps so that they can be expressed with just a few dominant DCT coefficients. This allows for the model to be built to estimate just the dominant spatial features of the 2D heatmaps, rather than the entire heatmap images, making it significantly more efficient. Experimental results from two commercial chips show that RealMaps can estimate the full-chip heatmaps with 0.9C and 1.2C root-mean-square-error respectively and take only 0.4ms for each inference which suits well for real-time use. Compared to the state of the art pre-silicon approach, RealMaps shows similar accuracy, but with much less computational cost.more » « less
-
In tis work, we propose a novel approach to real-time estimation of full-chip transient heatmaps for commercial processors based on machine learning. The model derived in this work supplements the temperature data sensed from the existing on-chip sensors, allowing for the development of more robust runtime power and thermal control schemes that can take advantage of the additional thermal information that is otherwise not available. The new approach involves offline acquisition of accurate spatial and temporal heatmaps using an infrared thermal imaging setup while nominal working conditions are maintained on the chip. To build the dynamic thermal model, we apply Long-Short-Term-Memory (LSTM) neutral networks with system-level variables such as chip frequency, instruction counts, and other performance metrics as inputs. To reduce the dimensionality of the model, 2D spatial discrete cosine transformation (DCT) is first performed on the heatmaps so that they can be expressed with just their dominant DCT frequencies. Our study shows that only $6\times 6$ DCT coefficients are required to maintain sufficient accuracy across a variety of workloads. Experimental results show that the proposed approach can estimate the full-chip heatmaps with less than 1.4C root-mean-square-error and take only 19ms for each inference which suits well for real-time use.more » « less
-
Neuromorphic computing systems execute machine learning tasks designed with spiking neural networks. These systems are embracing non-volatile memory to implement high-density and low-energy synaptic storage. Elevated voltages and currents needed to operate non-volatile memories cause aging of CMOS-based transistors in each neuron and synapse circuit in the hardware, drifting the transistor’s parameters from their nominal values. If these circuits are used continuously for too long, the parameter drifts cannot be reversed, resulting in permanent degradation of circuit performance over time, eventually leading to hardware faults. Aggressive device scaling increases power density and temperature, which further accelerates the aging, challenging the reliable operation of neuromorphic systems. Existing reliability-oriented techniques periodically de-stress all neuron and synapse circuits in the hardware at fixed intervals, assuming worst-case operating conditions, without actually tracking their aging at run-time. To de-stress these circuits, normal operation must be interrupted, which introduces latency in spike generation and propagation, impacting the inter-spike interval and hence, performance (e.g., accuracy). We observe that in contrast to long-term aging, which permanently damages the hardware, short-term aging in scaled CMOS transistors is mostly due to bias temperature instability. The latter is heavily workload-dependent and, more importantly, partially reversible. We propose a new architectural technique to mitigate the aging-related reliability problems in neuromorphic systems by designing an intelligent run-time manager (NCRTM), which dynamically de-stresses neuron and synapse circuits in response to the short-term aging in their CMOS transistors during the execution of machine learning workloads, with the objective of meeting a reliability target. NCRTM de-stresses these circuits only when it is absolutely necessary to do so, otherwise reducing the performance impact by scheduling de-stress operations off the critical path. We evaluate NCRTM with state-of-the-art machine learning workloads on a neuromorphic hardware. Our results demonstrate that NCRTM significantly improves the reliability of neuromorphic hardware, with marginal impact on performance.more » « less
-
Cough detection can provide an important marker to monitor chronic respiratory conditions. However, manual techniques which require human expertise to count coughs are both expensive and time-consuming. Recent Automatic Cough Detection Algorithms (ACDAs) have shown promise to meet clinical monitoring requirements, but only in recent years they have made their way to non-clinical settings due to the required portability of sensing technologies and the extended duration of data recording. More precisely, these ACDAs operate at high sampling frequencies, which leads to high power consumption and computing requirements, making these difficult to implement on a wearable device. Additionally, reproducibility of their performance is essential. Unfortunately, as the majority of ACDAs were developed using private clinical data, it is difficult to reproduce their results. We, hereby, present an ACDA that meets clinical monitoring requirements and reliably operates at a low sampling frequency. This ACDA is implemented using a convolutional neural network (CNN), and publicly available data. It achieves a sensitivity of 92.7%, a specificity of 92.3%, and an accuracy of 92.5% using a sampling frequency of just 750 Hz. We also show that a low sampling frequency allows us to preserve patients’ privacy by obfuscating their speech, and we analyze the trade-off between speech obfuscation for privacy and cough detection accuracy. Clinical relevance—This paper presents a new cough detection technique and preliminary analysis on the trade-off between detection accuracy and obfuscation of speech for privacy. These findings indicate that, using a publicly available dataset, we can sample signals at 750 Hz while still maintaining a sensitivity above 90%, suggested to be sufficient for clinical monitoring [1].more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
