Papers for

neuromorphic hardware developers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Time based readout improves energy efficiency of analog neural networks

A Time-Based Readout for Vector-Matrix Multiplication in Fully Analog Memristive SNNs

Abstract: Artificial neural networks rely on vector-matrix multiplications (VMMs), whose implementation in von Neumann architectures is dominated by costly data movement between memory and processing units. Spiking neural networks (SNNs) mitigate this bottleneck by performing in-memory, analog VMMs using memristive crossbar arrays. However, conventional current-mode readout circuits incur significant area and power overhead. This work proposes a fully analog readout architecture based on voltage-to-time conversion of the VMM output. By sensing the column voltage, the proposed approach avoids current-mode summing and scaling circuitry, improving area and energy efficiency. Post-layout simulations of a 10x1 SNN implemented in a 130 nm CMOS technology validate the proposed architecture, while application to a trained 64x10 SNN for digit classification further demonstrates its feasibility for SNN inference.

Thu 10 SeptEmerging TechnologiesArtificial IntelligenceHardware Architecture
The gist
Artificial neural networks rely on math operations called vector-matrix multiplications, which are usually slow and power-hungry because of moving data around. The authors focus on spiking neural networks that compute these operations inside memory using special devices called memristors, but traditional ways of reading the results waste space and energy. They propose a new way that converts voltage into time signals to read results more efficiently without complex circuits. Their tests show this method works well for digit recognition tasks and can save space and power.
Open → 2609.11713v1

Spiking neural networks partition inputs with richer patterns than ReLU nets

Polyhedral Geometry of Time-to-First-Spike Neural Networks

Abstract: We study the expressivity of spiking neural networks, which provide a natural framework for asynchronous, event-driven computation complementary to conventional feedforward neural networks. We consider the time-to-first-spike model in a setting for which the input-output map is continuous and piecewise linear, with affine pieces governed by causal feasibility constraints that determine which presynaptic spikes occur before a neuron fires. We first show that each neuron's firing time admits a maxout-like representation with exponentially many, highly constrained affine pieces. We then formalize causal regions as polyhedral regions with fixed causal sets and derive upper and lower bounds on the maximal number of causal regions in both shallow and multilayer feedforward spiking networks. Our theoretical and experimental results show that spiking networks can generate richer partitions of the input space than conventional feedforward ReLU networks.

Thu 10 SeptMachine Learning
The gist
This paper explores how spiking neural networks, which use timing of neuron spikes to process information, can represent input-output relationships differently than traditional neural networks. The authors studied a model where each neuron’s firing time is determined by patterns of input spike order, forming complex regions in input space. They mathematically describe these regions as shapes called polyhedra and show that spiking networks can create more varied partitions of inputs than typical ReLU networks. This suggests spiking networks can be more expressive in how they map inputs to outputs.
Open → 2609.11227v1

Brain inspired method solves neural timing learning problem

A Gradient-based yet Spike-Timing-Dependent Solution to the Feedback Learning Problem in Neural Microcircuits

Abstract: The brain uses discrete spikes for dynamic computation, yet, how neural microcircuits (NMCs) solve temporal credit assignment using local spike timing remains a fundamental open question. Dominant spiking neural network (SNN) approaches circumvent this by approximating backpropagation through surrogate gradients, decoupling learning from biological spike timing. Here, we reformulate temporal credit assignment as a state separation problem: extracting task-required components induced by historical perturbations directly from the current neural state. This enables an online feedback learning framework for NMCs through a gradient tunneling (GT) algorithm and the lead-lag expansion technique that derives credit assignment from local synaptic spike timing, while remaining compatible with ANN-SNN hybrid architectures. Experimentally, GT-trained NMCs excel at long-timescale evidence integration and noise-robust memory retention, and perform comparably to leading SNN online learning methods on real-world benchmarks with far fewer parameters. The proposed framework addresses the two-decade-old NMC feedback learning problem and suggests a computationally plausible explanation for the brain's learning mechanisms.

Tue 8 SeptNeural and Evolutionary ComputingMachine Learning
The gist
Figuring out how brain cells learn from the timing of their spikes has been a mystery for decades. The authors propose a new way to think about this learning as separating useful signals in the cells' current state. They develop a method that uses the timing of spikes to assign credit or blame for outcomes, helping neural circuits learn efficiently. Their approach matches or beats other methods while using fewer resources and might explain how real brains learn.
Open → 2609.08070v1