Search

Always-on keyword spotting (KWS) hardware is gaining popularity in ultra-low power IoT applications where specific words are used to wake up and activate the power hungry downstream system. This work proposes a low power KWS engine with a power-optimized Mel-frequency cepstral coefficients (MFCC) feature extraction module and a memoryoptimized long short term memory (LSTM) accelerator. Our LSTM model is pruned and compressed to reduce 89% of model size and 76% of computation. The LSTM accelerator adopts the weight stationary dataflow to reduce energy. Our simulation using a 40nm CMOS process achieves a power consumption of 2.51 μW, which is at least 2× lower than the state-of-the-art.

Activation functions such as hyperbolic tangent (tanh) and logistic sigmoid (sigmoid) are critical computing elements in a long short term memory (LSTM) cell and network. These activation functions are non-linear, leading to challenges in their hardware implementations. Area-efficient and high performance hardware implementation of these activation functions thus becomes crucial to allow high throughput in a LSTM accelerator. In this work, we propose an approximation scheme which is suitable for both tanh and sigmoid functions. The proposed hardware for sigmoid function is 8.3 times smaller than the state-of-the-art, while for tanh function, it is the second smallest design. When applying the approximated tanh and sigmoid of 2% error in a LSTM cell computation, its final hidden state and cell state record errors of 3.1% and 5.8% respectively. When the same approximated functions are applied to a single layer LSTM network of 64 hidden nodes, the accuracy drops by 2.8% only. This proposed small yet accurate activation function hardware is promising to be used in Internet of Things (IoT) applications where accuracy can be traded off for ultra-low power consumption. Accepted version We thank the Programmatic grant no. A1687b0033, Singapore RIE 2020, AME domain.

High performance but computationally expensive Convolutional Neural Networks (CNNs) require both algorithmic and custom hardware improvement to reduce model size and to improve energy efficiency for edge computing applications. Recent CNN architectures employ depthwise separable convolution to reduce the total number of weights and MAC operations. However, depthwise separable convolution workload does not run efficiently in existing CNN accelerators. This paper proposes an energy-efficient CONV unit for pointwise and depthwise operation. The CONV unit utilizes weight stationary to enable high efficiency. The row partial sum reduction is engaged to increase parallelism in pointwise convolution thereby lightening the memory requirements on output partial sums. Our design achieves a maximum efficiency of 3.17 TOPS/W at 0.85V/40nm CMOS which is well-suited for energy constrained edge computing applications. Accepted version

In the era of digital world, Convolutional Neural-network is widely adopted. To keep the network in a reasonable size, down-sampling is required. Max-pooling is a common solution for down-sampling. High throughput and area-efficient -hardware design of 2D max-pooling is essential for CNN accelerator. In this paper, we propose a highly efficient hardware design of max-pooling for 2D data input. The proposed design can be integrated into the neural-network accelerator as it can process the 2D input feature map data in parallel. The proposed solution, which is supporting up to 31 parallel data input, has been synthesized using at 40nm process node with area of 90×100 µm2, and the estimated power is 2.9 mW @200Mhz @1.1V power supply.

Tapered microchannel is a channel configuration design (CCD) which has gained attention owing to the superior performance in promoting uniform temperature distribution in a microchannel. In this study, the thermal-hydraulic performance of four different tapered channels is evaluated against a straight microchannel. The average hydraulic diameter, conductive heat transfer in the substrate material and convective heat transfer area are kept constant for the microchannels. The experiment is conducted for steady-state convective heat transfer using distilled water as the working fluid for Reynolds number range of 1300–3400 and a constant heat flux of 53.0 W/cm2. Results show that highly-tapered microchannels promote early turbulence at lower Reynolds numbers with better heat transfer and much greater pressure drop. All the tapered microchannels possess a thermal-hydraulic performance index which is less than 1, implying that a tapered microchannel yields a lower heat transfer capability given the same pumping power as a straight microchannel.