7 research outputs found
COMET: A Cross-Layer Optimized Optical Phase Change Main Memory Architecture
Traditional DRAM-based main memory systems face several challenges with
memory refresh overhead, high latency, and low throughput as the industry moves
towards smaller DRAM cells. These issues have been exacerbated by the emergence
of data-intensive applications in recent years. Memories based on phase change
materials (PCMs) offer promising solutions to these challenges. PCMs store data
in the material's phase, which can shift between amorphous and crystalline
states when external thermal energy is supplied. This is often achieved using
electrical pulses. Alternatively, using laser pulses and integration with
silicon photonics offers a unique opportunity to realize high-bandwidth and
low-latency photonic memories. Such a memory system may in turn open the
possibility of realizing fully photonic computing systems. But to realize
photonic memories, several challenges that are unique to the photonic domain
such as crosstalk, optical loss management, and laser power overhead have to be
addressed. In this work, we present COMET, the first cross-layer optimized
optical main memory architecture that uses PCMs. In architecting COMET, we
explore how to use silicon photonics and PCMs together to design a large-scale
main memory system while addressing associated challenges. We explore
challenges and propose solutions at the PCM cell, photonic memory circuit, and
memory architecture levels. Based on our evaluations, COMET offers 7.1x better
bandwidth, 15.1x lower EPB, and 3x lower latencies than the best-known prior
work on photonic main memory architecture design
GHOST: A Graph Neural Network Accelerator using Silicon Photonics
Graph neural networks (GNNs) have emerged as a powerful approach for
modelling and learning from graph-structured data. Multiple fields have since
benefitted enormously from the capabilities of GNNs, such as recommendation
systems, social network analysis, drug discovery, and robotics. However,
accelerating and efficiently processing GNNs require a unique approach that
goes beyond conventional artificial neural network accelerators, due to the
substantial computational and memory requirements of GNNs. The slowdown of
scaling in CMOS platforms also motivates a search for alternative
implementation substrates. In this paper, we present GHOST, the first
silicon-photonic hardware accelerator for GNNs. GHOST efficiently alleviates
the costs associated with both vertex-centric and edge-centric operations. It
implements separately the three main stages involved in running GNNs in the
optical domain, allowing it to be used for the inference of various widely used
GNN models and architectures, such as graph convolution networks and graph
attention networks. Our simulation studies indicate that GHOST exhibits at
least 10.2x better throughput and 3.8x better energy efficiency when compared
to GPU, TPU, CPU and multiple state-of-the-art GNN hardware accelerators
A Silicon Photonic Accelerator for Convolutional Neural Networks with Heterogeneous Quantization
Parameter quantization in convolutional neural networks (CNNs) can help
generate efficient models with lower memory footprint and computational
complexity. But, homogeneous quantization can result in significant degradation
of CNN model accuracy. In contrast, heterogeneous quantization represents a
promising approach to realize compact, quantized models with higher inference
accuracies. In this paper, we propose HQNNA, a CNN accelerator based on
non-coherent silicon photonics that can accelerate both homogeneously quantized
and heterogeneously quantized CNN models. Our analyses show that HQNNA achieves
up to 73.8x better energy-per-bit and 159.5x better throughput-energy
efficiency than state-of-the-art photonic CNN accelerators