Enabling Intelligent IoTs for Histopathology Image Analysis Using Convolutional Neural Networks

Medical imaging is an essential data source that has been leveraged worldwide in healthcare systems. In pathology, histopathology images are used for cancer diagnosis, whereas these images are very complex and their analyses by pathologists require large amounts of time and effort. On the other hand, although convolutional neural networks (CNNs) have produced near-human results in image processing tasks, their processing time is becoming longer and they need higher computational power. In this paper, we implement a quantized ResNet model on two histopathology image datasets to optimize the inference power consumption. We analyze classification accuracy, energy estimation, and hardware utilization metrics to evaluate our method. First, the original RGBcolored images are utilized for the training phase, and then compression methods such as channel reduction and sparsity are applied. Our results show an accuracy increase of 6% from RGB on 32-bit (baseline) to the optimized representation of sparsity on RGB with a lower bit-width, i.e., \u3c8:8\u3e. For energy estimation on the used CNN model, we found that the energy used in RGB color mode with 32-bit is considerably higher than the other lower bit-width and compressed color modes. Moreover, we show that lower bit-width implementations yield higher resource utilization and a lower memory bottleneck ratio. This work is suitable for inference on energy-limited devices, which are increasingly being used in the Internet of Things (IoT) systems that facilitate healthcare systems

Imc: Energy-Eicient In-Memory Convolver For Accelerating Binarized Deep Neural Network

Deep Convolutional Neural Networks (CNNs) are widely employed in modern AI systems due to their unprecedented accuracy in object recognition and detection. However, it has been proven that the main bottleneck to improve large scale deep CNN based hardware implementation performance is massive data communication between processing units and o-chip memory. In this paper, we pave a way towards novel concept of in-memory convolver (IMC) that could implement the dominant convolution computation within main memory based on our proposed Spin Orbit Torque Magnetic Random Access Memory (SOT-MRAM) array architecture to greatly reduce data communication and thus accelerate Binary CNN (BCNN). The proposed architecture could simultaneously work as non-volatile memory and a recongurable in-memory logic (AND, OR) without add-on logic circuits to memory chip as in conventional logic-in-memory designs. The computed logic output could be also simply read out like a normal MRAM bit-cell using the shared memory peripheral circuits. We employ such intrinsic in-memory processing architecture to eciently process data within memory to greatly reduce power-hungry and long distance data communication concerning state-of-the-art BCNN hardware. The hardware mapping results show that IMC can process the Binarized AlexNet on ImageNet data-set favorably with 134.27 J/img where ∼ 16× and 9× lower energy and area are achieved, respectively, compared to RRAM-based BCNN. Furthermore, 21.5% reduction in data movement in term of main memory accesses is observed compared to CPU/DRAM baseline

Pima-Logic: A Novel Processing-In-Memory Architecture For Highly Flexible And Energy-Efficient Logic Computation

In this paper, we propose PIMA-Logic, as a novel Processing-in-Memory Architecture for highly flexible and efficient Logic computation. Instead of integrating complex logic units in cost-sensitive memory, PIMA-Logic exploits a hardware-friendly approach to implement Boolean logic functions between operands either located in the same row or the same column within entire memory arrays. Furthermore, it can efficiently process more complex logic functions between multiple operands to further reduce the latency and power-hungry data movement. The proposed architecture is developed based on Spin Orbit Torque Magnetic Random Access Memory (SOT-MRAM) array and it can simultaneously work as a non-volatile memory and a reconfigurable in-memory logic. The device-to-architecture co-simulation results show that PIMA-Logic can achieve up to 56% and 31.6% improvements with respect to overall energy and delay on combinational logic benchmarks compared to recent Pinatubo architecture. We further implement an in-memory data encryption engine based on PIMA-Logic as a case study. With AES application, it shows 77.2% and 21% lower energy consumption compared to CMOS-ASIC and recent RIMPA implementation, respectively

Dima: A Depthwise Cnn In-Memory Accelerator

In this work, we first propose a deep depthwise Convolutional Neural Network (CNN) structure, called Add-Net, which uses binarized depthwise separable convolution to replace conventional spatial-convolution. In Add-Net, the computationally expensive convolution operations (i.e. Multiplication and Accumulation) are converted into hardware-friendly Addition operations. We meticulously investigate and analyze the Add-Net\u27s performance (i.e. accuracy, parameter size and computational cost) in object recognition application compared to traditional baseline CNN using the most popular large scale ImageNet dataset. Accordingly, we propose a Depthwise CNN In-Memory Accelerator (DIMA) based on SOT-MRAM computational sub-arrays to efficiently accelerate Add-Net within non-volatile MRAM. Our device-to-architecture co-simulation results show that, with almost the same inference accuracy to the baseline CNN on different data-sets, DIMA can obtain ∼1.4× better energy-efficiency and 15.7× speedup compared to ASICs, and, ∼1.6× better energy-efficiency and 5.6× speedup over the best processing-in-DRAM accelerators

Enabling Intelligent IoTs for Histopathology Image Analysis Using Convolutional Neural Networks

Medical imaging is an essential data source that has been leveraged worldwide in healthcare systems. In pathology, histopathology images are used for cancer diagnosis, whereas these images are very complex and their analyses by pathologists require large amounts of time and effort. On the other hand, although convolutional neural networks (CNNs) have produced near-human results in image processing tasks, their processing time is becoming longer and they need higher computational power. In this paper, we implement a quantized ResNet model on two histopathology image datasets to optimize the inference power consumption. We analyze classification accuracy, energy estimation, and hardware utilization metrics to evaluate our method. First, the original RGB-colored images are utilized for the training phase, and then compression methods such as channel reduction and sparsity are applied. Our results show an accuracy increase of 6% from RGB on 32-bit (baseline) to the optimized representation of sparsity on RGB with a lower bit-width, i.e., <8:8>. For energy estimation on the used CNN model, we found that the energy used in RGB color mode with 32-bit is considerably higher than the other lower bit-width and compressed color modes. Moreover, we show that lower bit-width implementations yield higher resource utilization and a lower memory bottleneck ratio. This work is suitable for inference on energy-limited devices, which are increasingly being used in the Internet of Things (IoT) systems that facilitate healthcare systems

Investigating microstructure and mechanical properties of aluminum matrix reinforced-graphene nanosheets composites fabricated by mechanical milling and equal channel angular pressing

A few layer graphene reinforced metal matrix nanocomposites with excellent mechanical properties and low density are a new class of advanced materials for a broad range of applications. A facile three steps approach based on ultra-sonication for dispersion of graphene nanosheets (GNSs), ball milling for Al powder mixing with different GNSs weight percent and equal channel angular pressing for powders consolidation at 200°C, has been applied for nanocomposites fabrication. The Raman analysis revealed that the GNSs in the sample with 0.25 wt.% were exfoliated by the creation of some defects and disordering. X-ray diffraction and microstructural analysis confirmed that the interaction of GNSs and matrix was almost mechanical interfacial bonding. Density test demonstrated that all samples except 1 wt.% GNSs were fully densified due to the formation of microvoids, which was observed in scanning electron microscope analysis. Investigation of mechanical properties showed that by using Al powders with commercial purity, 0.25 wt.% sample possessed the maximum hardness, ultimate shear strength and uniform normal displacement in comparison with other samples. Highest mechanical properties which was observed in 0.25 wt.% GNSs composite, resulting from the embedding of exfoliated GNSs between Al powders, excellent mechanical bonding and grain refinement. Oppositely, agglomerated GNSs and existence of microvoids caused deterioration of mechanical properties in 1 wt.% sample