arXiv subjects

Rajit Manohar

Publications and source records attributed to Rajit Manohar.

8 recordsLinked to original sources

arXiv2026

Neuromorphic architectures as numerical solvers for computational neuroscience

Neuromorphic computing is closely associated with spiking neuronal networks. However, an alternative class of so-called "rate-based" models arising from computational neuroscience and machine learning forgoes spiking interactions and instead relies on continuous coupling between neurons. Existing neuromorphic implementations designed around spike-based interactions are not well-suited for emulating such models. Here view the distributed simulation of these models as message-passing algorithms on parallel hardware. Leveraging prior art in numerical algorithms and distributed simulation, we outline steps that enable the design of efficient digital neuromorphic accelerators for non-spiking neuronal models. In particular, we show that multi-bit packets, rather than spikes, are the most efficient communication strategy in packet-switched networks and that compared to basic numerical integration methods, higher-order differential equation solvers decrease both computation and communication costs while achieving lower numerical error, but that these benefits are ultimately limited by arithmetic precision. Using our proposed design principles, we convert an existing neuromorphic architecture into a distributed numerical solver - a spikeless neuromorphic system - for continuously-coupled neuronal models. We thereby demonstrate that our theoretical considerations indeed translate into practical advantages, namely reduced energy consumption and delay.

cs.AR↗

arXiv2023

FPIM: Field-Programmable Ising Machines for Solving SAT

On-chip analog Ising Machines (IMs) are a promising means to solve difficult combinatorial optimization problems. For scalable on-chip realizations to be practical, 1) the problem should map scalably to Ising form, 2) interconnectivity between spins should be sparse, 3) the number of bits of coupling resolution (BCR) needed for programming interconnection weights should be small, and 4) the chip should be capable of solving problems with different connection topologies. We explore these issues for the SATisfiability problem and devise FPIM, a reconfigurable on-chip analog Ising machine scheme well suited for SAT. To map SAT problems onto FPIMs, we leverage Boolean logic synthesis as a first step, but replace synthesized logic gates with Ising equivalent circuits whose analog dynamics solve SAT by minimizing the Ising Hamiltonian. We apply our approach to 2000 benchmark problems from SATLIB,demonstrating excellent scaling, together with low sparsity and low BCR that are independent of problem scale. Placement/routing reveals a very feasible requirement of less than 10 routing tracks to implement all the benchmarks, translating to an area requirement of about 10mm^2 for a programmable 1000-spin FPIM in 65nm technology.

cs.ET↗

arXiv2023

Opportunistic Mutual Exclusion

Mutual exclusion is an important problem in the context of shared resource usage, where only one process can be using the shared resource at any given time. A mutual exclusion protocol that does not use information on the duration for which each process uses the resource can lead to sub-optimal utilization times. We consider a simple two-process mutual exclusion problem with a central server that provides access to the shared resource. We show that even in the absence of a clock, under certain conditions, the server can opportunistically grant early access to a client based on timing information. We call our new protocol opportunistic mutual exclusion. Our approach requires an extra request signal on each channel between client and server to convey extra information, and the server can grant early access based only on the order of events rather than through measuring time. We derive the handshaking specification and production rules for our protocol, and report on the energy and delay of the circuits in a 65nm process.

cs.DC↗

arXiv2023

NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems

Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accurately measure technological advancements, compare performance with conventional methods, and identify promising future research directions. Prior neuromorphic computing benchmark efforts have not seen widespread adoption due to a lack of inclusive, actionable, and iterative benchmark design and guidelines. To address these shortcomings, we present NeuroBench: a benchmark framework for neuromorphic computing algorithms and systems. NeuroBench is a collaboratively-designed effort from an open community of researchers across industry and academia, aiming to provide a representative structure for standardizing the evaluation of neuromorphic approaches. The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings. In this article, we outline tasks and guidelines for benchmarks across multiple application domains, and present initial performance baselines across neuromorphic and conventional approaches for both benchmark tracks. NeuroBench is intended to continually expand its benchmarks and features to foster and track the progress made by the research community.

cs.AI↗

arXiv2023

A Multi-Site Accelerator-Rich Processing Fabric for Scalable Brain-Computer Interfacing

Hull is an accelerator-rich distributed implantable Brain-Computer Interface (BCI) that reads biological neurons at data rates that are 2-3 orders of magnitude higher than the prior state of art, while supporting many neuroscientific applications. Prior approaches have restricted brain interfacing to tens of megabits per second in order to meet two constraints necessary for effective operation and safe long-term implantation -- power dissipation under tens of milliwatts and response latencies in the tens of milliseconds. Hull also adheres to these constraints, but is able to interface with the brain at much higher data rates, thereby enabling, for the first time, BCI-driven research on and clinical treatment of brain-wide behaviors and diseases that require reading and stimulating many brain locations. Central to Hull's power efficiency is its realization as a distributed system of BCI nodes with accelerator-rich compute. Hull balances modular system layering with aggressive cross-layer hardware-software co-design to integrate compute, networking, and storage. The result is a lesson in designing networked distributed systems with hardware accelerators from the ground up.

cs.DC↗

arXiv2018

The Impact of On-chip Communication on Memory Technologies for Neuromorphic Systems

Emergent nanoscale non-volatile memory technologies with high integration density offer a promising solution to overcome the scalability limitations of CMOS-based neural networks architectures, by efficiently exhibiting the key principle of neural computation. Despite the potential improvements in computational costs, designing high-performance on-chip communication networks that support flexible, large-fanout connectivity remains as daunting task. In this paper, we elaborate on the communication requirements of large-scale neuromorphic designs, and point out the differences with the conventional network-on-chip architectures. We present existing approaches for on-chip neuromorphic routing networks, and discuss how new memory and integration technologies may help to alleviate the communication issues in constructing next-generation intelligent computing machines.

cs.AR↗

arXiv2017

Energy-efficient Hybrid CMOS-NEMS LIF Neuron Circuit in 28 nm CMOS Process

Designing analog sub-threshold neuromorphic circuits in deep sub-micron technologies e.g. 28 nm can be a daunting task due to the problem of excessive leakage current. We propose novel energy-efficient hybrid CMOS-nano electro-mechanical switches (NEMS) Leaky Integrate and Fire (LIF) neuron and synapse circuits and investigate the impact of NEM switches on the leakage power and overall energy consumption. We analyze the performance of biologically-inspired neuron circuit in terms of leakage power consumption and present new energy-efficient neural circuits that operate with biologically plausible firing rates. Our results show the proposed CMOS-NEMS neuron circuit is, on average, 35% more energy-efficient than its CMOS counterpart with same complexity in 28 nm process. Moreover, we discuss how NEM switches can be utilized to further improve the scalability of mixed-signal neuromorphic circuits.

cs.ET↗

arXiv2017

On Using Time Without Clocks via Zigzag Causality

Even in the absence of clocks, time bounds on the duration of actions enable the use of time for distributed coordination. This paper initiates an investigation of coordination in such a setting. A new communication structure called a zigzag pattern is introduced, and shown to guarantee bounds on the relative timing of events in this clockless model. Indeed, zigzag patterns are shown to be necessary and sufficient for establishing that events occur in a manner that satisfies prescribed bounds. We capture when a process can know that an appropriate zigzag pattern exists, and use this to provide necessary and sufficient conditions for timed coordination of events using a full-information protocol in the clockless model.

cs.DC↗