70 research outputs found
RISA: Round-Robin Intra-Rack Friendly Scheduling Algorithm for Disaggregated Datacenters
Recent trends see a move away from a fixed-resource server-centric datacenter
model to a more adaptable "disaggregated" datacenter model. These disaggregated
datacenters can then dynamically group resources to the specific requirements
of an incoming workload, thereby improving efficiency. To properly utilize
these disaggregated datacenters, workload allocation techniques must examine
the current state of the datacenter and choose resources that not only optimize
the current workload request, but future ones. Since disaggregated datacenters
are severely bottlenecked by the available network resources, our work proposes
a heuristic-based approach called RISA, which significantly reduces the network
usage of workload allocations in disaggregated datacenters. Compared to the
state-of-the-art, RISA reduces the power consumption for optical components by
33% and reduces the average CPU-RAM round-trip latency by 50%. Additionally,
RISA significantly outperforms the state-of-the-art in terms of execution time.Comment: Clarified some prior work and their citation
Characterizing Coherent Integrated Photonic Neural Networks under Imperfections
Integrated photonic neural networks (IPNNs) are emerging as promising
successors to conventional electronic AI accelerators as they offer substantial
improvements in computing speed and energy efficiency. In particular, coherent
IPNNs use arrays of Mach-Zehnder interferometers (MZIs) for unitary
transformations to perform energy-efficient matrix-vector multiplication.
However, the underlying MZI devices in IPNNs are susceptible to uncertainties
stemming from optical lithographic variations and thermal crosstalk and can
experience imprecisions due to non-uniform MZI insertion loss and quantization
errors due to low-precision encoding in the tuned phase angles. In this paper,
we, for the first time, systematically characterize the impact of such
uncertainties and imprecisions (together referred to as imperfections) in IPNNs
using a bottom-up approach. We show that their impact on IPNN accuracy can vary
widely based on the tuned parameters (e.g., phase angles) of the affected
components, their physical location, and the nature and distribution of the
imperfections. To improve reliability measures, we identify critical IPNN
building blocks that, under imperfections, can lead to catastrophic degradation
in the classification accuracy. We show that under multiple simultaneous
imperfections, the IPNN inferencing accuracy can degrade by up to 46%, even
when the imperfection parameters are restricted within a small range. Our
results also indicate that the inferencing accuracy is sensitive to
imperfections affecting the MZIs in the linear layers next to the input layer
of the IPNN.Comment: This paper has been accepted for publication in the IEEE Journal of
Lightwave Technology (JLT
Design Space Exploration for PCM-based Photonic Memory
The integration of silicon photonics (SiPh) and phase change materials (PCMs)
has created a unique opportunity to realize adaptable and reconfigurable
photonic systems. In particular, the nonvolatile programmability in PCMs has
made them a promising candidate for implementing optical memory systems. In
this paper, we describe the design of an optical memory cell based on PCMs
while exploring the design space of the cell in terms of PCM material choice
(e.g., GST, GSST, Sb2Se3), cell bit capacity, latency, and power consumption.
Leveraging this design-space exploration for the design of efficient optical
memory cells, we present the design and implementation of an optical memory
array and explore its scalability and power consumption when using different
optical memory cells. We also identify performance bottlenecks that need to be
alleviated to further scale optical memory arrays with competitive latency and
energy consumption, compared to their electronic counterparts.Comment: This paper will appear in the proceedings of ACM GLSVLSI 202
Integrated Photonic AI Accelerators under Hardware Security Attacks: Impacts and Countermeasures
Integrated photonics based on silicon photonics platform is driving several
application domains, from enabling ultra-fast chip-scale communication in
high-performance computing systems to energy-efficient optical computation in
artificial intelligence (AI) hardware accelerators. Integrating silicon
photonics into a system necessitates the adoption of interfaces between the
photonic and the electronic subsystems, which are required for buffering data
and optical-to-electrical and electrical-to-optical conversions. Consequently,
this can lead to new and inevitable security breaches that cannot be fully
addressed using hardware security solutions proposed for purely electronic
systems. This paper explores different types of attacks profiting from such
breaches in integrated photonic neural network accelerators. We show the impact
of these attacks on the system performance (i.e., power and phase
distributions, which impact accuracy) and possible solutions to counter such
attacks
SerIOS: Enhancing Hardware Security in Integrated Optoelectronic Systems
Silicon photonics (SiPh) has different applications, from enabling fast and
high-bandwidth communication for high-performance computing systems to
realizing energy-efficient optical computation for AI hardware accelerators.
However, integrating SiPh with electronic sub-systems can introduce new
security vulnerabilities that cannot be adequately addressed using existing
hardware security solutions for electronic systems. This paper introduces
SerIOS, the first framework aimed at enhancing hardware security in
optoelectronic systems by leveraging the unique properties of optical
lithography. SerIOS employs cryptographic keys generated based on imperfections
in the optical lithography process and an online detection mechanism to detect
attacks. Simulation and synthesis results demonstrate SerIOS's effectiveness in
detecting and preventing attacks, with a small area footprint of less than 15%
and a 100% detection rate across various attack scenarios and optoelectronic
architectures, including photonic AI accelerators
Compact and Low-Loss PCM-based Silicon Photonic MZIs for Photonic Neural Networks
We present an optimized Mach-Zehnder Interferometer (MZI) with phase change
materials for photonic neural networks (PNNs). With 0.2 dB loss, -38 dB
crosstalk, and length of 52 micrometer, the designed MZI significantly improves
the scalability and accuracy of PNNs under loss and crosstalk.Comment: This paper is accepted at IEEE Photonics Conference (IPC) 202
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