8 research outputs found
Demonstrating Analog Inference on the BrainScaleS-2 Mobile System
We present the BrainScaleS-2 mobile system as a compact analog inference
engine based on the BrainScaleS-2 ASIC and demonstrate its capabilities at
classifying a medical electrocardiogram dataset. The analog network core of the
ASIC is utilized to perform the multiply-accumulate operations of a
convolutional deep neural network. At a system power consumption of 5.6W, we
measure a total energy consumption of 192uJ for the ASIC and achieve a
classification time of 276us per electrocardiographic patient sample. Patients
with atrial fibrillation are correctly identified with a detection rate of
(93.70.7)% at (14.01.0)% false positives. The system is directly
applicable to edge inference applications due to its small size, power
envelope, and flexible I/O capabilities. It has enabled the BrainScaleS-2 ASIC
to be operated reliably outside a specialized lab setting. In future
applications, the system allows for a combination of conventional machine
learning layers with online learning in spiking neural networks on a single
neuromorphic platform
Demonstrating Advantages of Neuromorphic Computation: A Pilot Study
Neuromorphic devices represent an attempt to mimic aspects of the brain's
architecture and dynamics with the aim of replicating its hallmark functional
capabilities in terms of computational power, robust learning and energy
efficiency. We employ a single-chip prototype of the BrainScaleS 2 neuromorphic
system to implement a proof-of-concept demonstration of reward-modulated
spike-timing-dependent plasticity in a spiking network that learns to play the
Pong video game by smooth pursuit. This system combines an electronic
mixed-signal substrate for emulating neuron and synapse dynamics with an
embedded digital processor for on-chip learning, which in this work also serves
to simulate the virtual environment and learning agent. The analog emulation of
neuronal membrane dynamics enables a 1000-fold acceleration with respect to
biological real-time, with the entire chip operating on a power budget of 57mW.
Compared to an equivalent simulation using state-of-the-art software, the
on-chip emulation is at least one order of magnitude faster and three orders of
magnitude more energy-efficient. We demonstrate how on-chip learning can
mitigate the effects of fixed-pattern noise, which is unavoidable in analog
substrates, while making use of temporal variability for action exploration.
Learning compensates imperfections of the physical substrate, as manifested in
neuronal parameter variability, by adapting synaptic weights to match
respective excitability of individual neurons.Comment: Added measurements with noise in NEST simulation, add notice about
journal publication. Frontiers in Neuromorphic Engineering (2019
Inference with Artificial Neural Networks on Analog Neuromorphic Hardware
The neuromorphic BrainScaleS-2 ASIC comprises mixed-signal neurons and
synapse circuits as well as two versatile digital microprocessors. Primarily
designed to emulate spiking neural networks, the system can also operate in a
vector-matrix multiplication and accumulation mode for artificial neural
networks. Analog multiplication is carried out in the synapse circuits, while
the results are accumulated on the neurons' membrane capacitors. Designed as an
analog, in-memory computing device, it promises high energy efficiency.
Fixed-pattern noise and trial-to-trial variations, however, require the
implemented networks to cope with a certain level of perturbations. Further
limitations are imposed by the digital resolution of the input values (5 bit),
matrix weights (6 bit) and resulting neuron activations (8 bit). In this paper,
we discuss BrainScaleS-2 as an analog inference accelerator and present
calibration as well as optimization strategies, highlighting the advantages of
training with hardware in the loop. Among other benchmarks, we classify the
MNIST handwritten digits dataset using a two-dimensional convolution and two
dense layers. We reach 98.0% test accuracy, closely matching the performance of
the same network evaluated in software
The BrainScaleS-2 Neuromorphic Platform — A Report on the Integration and Operation of an Open Science Hardware Platform within EBRAINS
This report presents the challenges encountered and the solutions created for the operation of the BrainScaleS neuromorphic platform, and the overall progress leading to this state at the end of the Human Brain Project (HBP)