Significance Driven Hybrid 8T-6T SRAM for Energy-Efficient Synaptic Storage in Artificial Neural Networks

Multilayered artificial neural networks (ANN) have found widespread utility in classification and recognition applications. The scale and complexity of such networks together with the inadequacies of general purpose computing platforms have led to a significant interest in the development of efficient hardware implementations. In this work, we focus on designing energy efficient on-chip storage for the synaptic weights. In order to minimize the power consumption of typical digital CMOS implementations of such large-scale networks, the digital neurons could be operated reliably at scaled voltages by reducing the clock frequency. On the contrary, the on-chip synaptic storage designed using a conventional 6T SRAM is susceptible to bitcell failures at reduced voltages. However, the intrinsic error resiliency of NNs to small synaptic weight perturbations enables us to scale the operating voltage of the 6TSRAM. Our analysis on a widely used digit recognition dataset indicates that the voltage can be scaled by 200mV from the nominal operating voltage (950mV) for practically no loss (less than 0.5%) in accuracy (22nm predictive technology). Scaling beyond that causes substantial performance degradation owing to increased probability of failures in the MSBs of the synaptic weights. We, therefore propose a significance driven hybrid 8T-6T SRAM, wherein the sensitive MSBs are stored in 8T bitcells that are robust at scaled voltages due to decoupled read and write paths. In an effort to further minimize the area penalty, we present a synaptic-sensitivity driven hybrid memory architecture consisting of multiple 8T-6T SRAM banks. Our circuit to system-level simulation framework shows that the proposed synaptic-sensitivity driven architecture provides a 30.91% reduction in the memory access power with a 10.41% area overhead, for less than 1% loss in the classification accuracy.Comment: Accepted in Design, Automation and Test in Europe 2016 conference (DATE-2016

Hardware-Algorithm Co-design Enabling Processing-in-Pixel-in-Memory (P2M) for Neuromorphic Vision Sensors

The high volume of data transmission between the edge sensor and the cloud processor leads to energy and throughput bottlenecks for resource-constrained edge devices focused on computer vision. Hence, researchers are investigating different approaches (e.g., near-sensor processing, in-sensor processing, in-pixel processing) by executing computations closer to the sensor to reduce the transmission bandwidth. Specifically, in-pixel processing for neuromorphic vision sensors (e.g., dynamic vision sensors (DVS)) involves incorporating asynchronous multiply-accumulate (MAC) operations within the pixel array, resulting in improved energy efficiency. In a CMOS implementation, low overhead energy-efficient analog MAC accumulates charges on a passive capacitor; however, the capacitor's limited charge retention time affects the algorithmic integration time choices, impacting the algorithmic accuracy, bandwidth, energy, and training efficiency. Consequently, this results in a design trade-off on the hardware aspect-creating a need for a low-leakage compute unit while maintaining the area and energy benefits. In this work, we present a holistic analysis of the hardware-algorithm co-design trade-off based on the limited integration time posed by the hardware and techniques to improve the leakage performance of the in-pixel analog MAC operations.Comment: 6 pages, 4 figures, 1 tabl

A Context-Switching/Dual-Context ROM Augmented RAM using Standard 8T SRAM

The landscape of emerging applications has been continually widening, encompassing various data-intensive applications like artificial intelligence, machine learning, secure encryption, Internet-of-Things, etc. A sustainable approach toward creating dedicated hardware platforms that can cater to multiple applications often requires the underlying hardware to context-switch or support more than one context simultaneously. This paper presents a context-switching and dual-context memory based on the standard 8T SRAM bit-cell. Specifically, we exploit the availability of multi-VT transistors by selectively choosing the read-port transistors of the 8T SRAM cell to be either high-VT or low-VT. The 8T SRAM cell is thus augmented to store ROM data (represented as the VT of the transistors constituting the read-port) while simultaneously storing RAM data. Further, we propose specific sensing methodologies such that the memory array can support RAM-only or ROM-only mode (context-switching (CS) mode) or RAM and ROM mode simultaneously (dual-context (DC) mode). Extensive Monte-Carlo simulations have verified the robustness of our proposed ROM-augmented CS/DC memory on the Globalfoundries 22nm-FDX technology node