skip to main content


Title: A Hybrid Optical-Electrical Analog Deep Learning Accelerator Using Incoherent Optical Signals
We present a hybrid optical-electrical analog deep learning (DL) accelerator, the first work to use incoherent optical signals for DL workloads. Incoherent optical designs are more attractive than coherent ones as the former can be more easily realized in practice. However, a significant challenge in analog DL accelerators, where multiply-accumulate operations are dominant, is that there is no known solution to perform accumulation using incoherent optical signals. We overcome this challenge by devising a hybrid approach: accumulation is done in the electrical domain, while multiplication is performed in the optical domain. The key technology enabler of our design is the transistor laser, which performs electrical-to-optical and optical-to-electrical conversions efficiently to tightly integrate electrical and optical devices into compact circuits. As such, our design fully realizes the ultra high-speed and high-energy-efficiency advantages of analog and optical computing. Our evaluation results using the MNIST benchmark show that our design achieves 2214× and 65× improvements in latency and energy, respectively, compared to a state-of-the-art memristor-based analog design.  more » « less
Award ID(s):
1640196
NSF-PAR ID:
10276957
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ; ; ;
Date Published:
Journal Name:
GLSVLSI '21: Proceedings of the 2021 on Great Lakes Symposium on VLSI
Page Range / eLocation ID:
271 to 276
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Time-frequency (TF) filtering of analog signals has played a crucial role in the development of radio-frequency communications and is currently being recognized as an essential capability for communications, both classical and quantum, in the optical frequency domain. How best to design optical time-frequency (TF) filters to pass a targeted temporal mode (TM), and to reject background (noise) photons in the TF detection window? The solution for ‘coherent’ TF filtering is known—the quantum pulse gate—whereas the conventional, more common method is implemented by a sequence of incoherent spectral filtering and temporal gating operations. To compare these two methods, we derive a general formalism for two-stage incoherent time-frequency filtering, finding expressions for signal pulse transmission efficiency, and for the ability to discriminate TMs, which allows the blocking of unwanted background light. We derive the tradeoff between efficiency and TM discrimination ability, and find a remarkably concise relation between these two quantities and the time-bandwidth product of the combined filters. We apply the formalism to two examples—rectangular filters or Gaussian filters—both of which have known orthogonal-function decompositions. The formalism can be applied to any state of light occupying the input temporal mode, e.g., ‘classical’ coherent-state signals or pulsed single-photon states of light. In contrast to the radio-frequency domain, where coherent detection is standard and one can use coherent matched filtering to reject noise, in the optical domain direct detection is optimal in a number of scenarios where the signal flux is extremely small. Our analysis shows how the insertion loss and SNR change when one uses incoherent optical filters to reject background noise, followed by direct detection, e.g. photon counting. We point out implications in classical and quantum optical communications. As an example, we study quantum key distribution, wherein strong rejection of background noise is necessary to maintain a high quality of entanglement, while high signal transmission is needed to ensure a useful key generation rate.

     
    more » « less
  2. Optical metasurfaces consist of densely arranged unit cells that manipulate light through various light confinement and scattering processes. Due to its unique advantages, such as high performance, small form factor and easy integration with semiconductor devices, metasurfaces have been gathering increasing attention in fields such as displays, imaging, sensing and optical computation. Despite advances in fabrication and characterization, a viable design prediction for suitable optical response remains challenging for complex optical metamaterial systems. The computation cost required to obtain the optimal design exponentially grows as the design complexity increases. Furthermore, the design prediction is challenging since the inverse problem is often ill-posed. In recent years, deep learning (DL) methods have shown great promise in the area of inverse design. Inspired by this and the capability of DL to produce fast inference, we introduce a physics-informed DL framework to expedite the computation for the inverse design of metasurfaces. Addition of the physics-based constraints improve generalizability of the DL model while reducing data burden. Our approach introduces a tandem DL architecture with physics-based learning to alleviate the nonuniqueness issue by selecting designs that are scientifically consistent, with low error in design prediction and accurate reconstruction of optical responses. To prove the concept, we focus on the inverse design of a representative plasmonic device that consists of metal gratings deposited on a dielectric film on top of a metal substrate. The optical response of the device is determined by the geometrical dimensions as well as the material properties. The training and testing data are obtained through Rigorous Coupled-Wave Analysis (RCWA), while the physics-based constraint is derived from solving the electromagnetic (EM) wave equations for a simplified homogenized model. We consider the prediction of design for the optical response of a single wavelength incident or a spectrum of wavelength in the visible light range. Our model converges with an accuracy up to 97% for inverse design prediction with the optical response for the visible light spectrum as input. The model is also able to predict design with accuracy up to 96% and optical response reconstruction accuracy of 99% for optical response of a single wavelength of light as input. 
    more » « less
  3. Phase Change Memory (PCM) is an attractive candidate for main memory, as it offers non-volatility and zero leakage power while providing higher cell densities, longer data retention time, and higher capacity scaling compared to DRAM. In PCM, data is stored in the crystalline or amorphous state of the phase change material. The typical electrically controlled PCM (EPCM), however, suffers from longer write latency and higher write energy compared to DRAM and limited multi-level cell (MLC) capacities. These challenges limit the performance of data-intensive applications running on computing systems with EPCMs.

    Recently, researchers demonstrated optically controlled PCM (OPCM) cells with support for 5bits/cellin contrast to 2bits/cellin EPCM. These OPCM cells can be accessed directly with optical signals that are multiplexed in high-bandwidth-density silicon-photonic links. The higher MLC capacity in OPCM and the direct cell access using optical signals enable an increased read/write throughput and lower energy per access than EPCM. However, due to the direct cell access using optical signals, OPCM systems cannot be designed using conventional memory architecture. We need a complete redesign of the memory architecture that is tailored to the properties of OPCM technology.

    This article presents the design of a unified network and main memory system called COSMOS that combines OPCM and silicon-photonic links to achieve high memory throughput. COSMOS is composed of a hierarchical multi-banked OPCM array with novel read and write access protocols. COSMOS uses an Electrical-Optical-Electrical (E-O-E) control unit to map standard DRAM read/write commands (sent in electrical domain) from the memory controller on to optical signals that access the OPCM cells. Our evaluation of a 2.5D-integrated system containing a processor and COSMOS demonstrates2.14 ×average speedup across graph and HPC workloads compared to an EPCM system. COSMOS consumes3.8×lower read energy-per-bit and5.97×lower write energy-per-bit compared to EPCM. COSMOS is the first non-volatile memory that provides comparable performance and energy consumption as DDR5 in addition to increased bit density, higher area efficiency, and improved scalability.

     
    more » « less
  4. Abstract

    Light’s ability to perform massive linear operations in parallel has recently inspired numerous demonstrations of optics-assisted artificial neural networks (ANN). However, a clear system-level advantage of optics over purely digital ANN has not yet been established. While linear operations can indeed be optically performed very efficiently, the lack of nonlinearity and signal regeneration require high-power, low-latency signal transduction between optics and electronics. Additionally, a large power is needed for lasers and photodetectors, which are often neglected in the calculation of the total energy consumption. Here, instead of mapping traditional digital operations to optics, we co-designed a hybrid optical-digital ANN, that operates on incoherent light, and is thus amenable to operations under ambient light. Keeping the latency and power constant between a purely digital ANN and a hybrid optical-digital ANN, we identified a low-power/latency regime, where an optical encoder provides higher classification accuracy than a purely digital ANN. We estimate our optical encoder enables ∼10 kHz rate operation of a hybrid ANN with a power of only 23 mW. However, in that regime, the overall classification accuracy is lower than what is achievable with higher power and latency. Our results indicate that optics can be advantageous over digital ANN in applications, where the overall performance of the ANN can be relaxed to prioritize lower power and latency.

     
    more » « less
  5. Abstract

    The last decades have witnessed substantial progress in optical technologies revolutionizing our ability to record and manipulate neural activity in genetically modified animal models. Meanwhile, human studies mostly rely on electrophysiological recordings of cortical potentials, which cannot be inferred from optical recordings, leading to a gap between our understanding of dynamics of microscale populations and brain‐scale neural activity. By enabling concurrent integration of electrical and optical modalities, transparent graphene microelectrodes can close this gap. However, the high impedance of graphene constitutes a big challenge toward the widespread use of this technology. Here, it is experimentally demonstrated that this high impedance of graphene microelectrodes is fundamentally limited by quantum capacitance. This quantum capacitance limit is overcome by creating a parallel conduction path using platinum nanoparticles. A 100 times reduction in graphene electrode impedance is achieved, while maintaining the high optical transparency crucial for deep two‐photon microscopy. Using a transgenic mouse model, simultaneous electrical recording of cortical activity with high fidelity is demonstrated while imaging calcium signals at various cortical depths right beneath the transparent microelectrodes. Multimodal analysis of Ca2+spikes and cortical surface potentials offers unique opportunities to bridge our understanding of cellular dynamics and brain‐scale neural activity.

     
    more » « less