Benchmark A standardized task that can be performed by disparate computing approaches, used to assess their relative processing merit in specific cases. Bifurcation A qualitative change in behavior of a dynamical system in response to parameter variation. Examples include cusp (from monostable to bistable), Hopf (from stable to oscillating), and transcritical (exchange of stability between two steady states). Brain-inspired computing (a.k.a. neuroinspired computing) A biologically inspired approach to build processors, devices, and computing models for applications including adaptive control, machine learning, and cognitive radio. Similarities with biological signal processing include architectural, such as distributed; representational, such as analog or spiking; or algorithmic, such as adaptation. Broadcast and Weight A multiwavelength analog networking protocol in which multiple all photonic neuron outputs are multiplexed and distributed to all neuron inputs. Weights are reconfigured by tunable spectral filters. Excitability A far-from-equilibrium nonlinear dynamical mechanism underlying all-or-none responses to small perturbations. Fan-in The number of inputs to a neuron. Layered network A network topology consisting of a series of sets (i.e., layers) of neurons. The neurons in each set project their outputs only to neurons in the subsequent layer. Most commonly used type of network used for machine learning. Metric A quantity assessing performance of a device in reference to a specific computing approach. Microring weight bank A silicon photonic implementation of a reconfigurable spectral filter capable of independently setting transmission at multiple carrier wavelengths. Modulation The act of representing an abstract variable in a physical quantity, such as photon rate (i.e., optical power), free carrier density (i.e., optical gain), and carrier drift (i.e., current). Electro-optic modulators are devices that convert from an electrical signal to the power envelope of an optical signal. Moore’s law An observation that the number of transistors in an integrated circuit doubles every 18 to 24 months, doubling its performance. Multiply-accumulate (MAC) A common operation that represents a single multiplication followed by an addition: a a + (b c). Neural networks A wide class of computing models consisting of a distributed set of nodes, called neurons, interconnected with configurable or adaptable strengths, called weights. Overall neural network behavior can
[1]
Harald Burgsteiner,et al.
ON LEARNING WITH RECURRENT SPIKING NEURAL NETWORKS AND THEIR APPLICATIONS TO ROBOT CONTROL WITH REAL-WORLD DEVICES
,
2005
.
[2]
L. Chrostowski,et al.
Silicon Photonics Design: From Devices to Systems
,
2015
.
[3]
A.H.M. van Roermund,et al.
Integrated 60GHz RF beamforming in CMOS
,
2011
.
[4]
Arthur A. Oliner,et al.
Phased array antennas
,
1972
.
[5]
G.E. Moore,et al.
Cramming More Components Onto Integrated Circuits
,
1998,
Proceedings of the IEEE.
[6]
Behzad Razavi,et al.
Design of Analog CMOS Integrated Circuits
,
1999
.
[7]
R. Douglas,et al.
Event-Based Neuromorphic Systems
,
2015
.
[8]
Bernard Brezzo,et al.
TrueNorth: Design and Tool Flow of a 65 mW 1 Million Neuron Programmable Neurosynaptic Chip
,
2015,
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.
[9]
Eugene M. Izhikevich,et al.
Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting
,
2006
.
[10]
Yue Tian,et al.
Photonic Neuromorphic Signal Processing and Computing
,
2014
.
[11]
Sander M. Bohte,et al.
Computing with Spiking Neuron Networks
,
2012,
Handbook of Natural Computing.
[12]
Thomas H. Lee,et al.
The Design of CMOS Radio-Frequency Integrated Circuits: RF CIRCUITS THROUGH THE AGES
,
2003
.