PyCARL: A PyNN Interface for Hardware-Software Co-Simulation of Spiking Neural Network

We present PyCARL, a PyNN-based common Python programming interface for hardware-software co-simulation of spiking neural network (SNN). Through PyCARL, we make the following two key contributions. First, we provide an interface of PyNN to CARLsim, a computationally-efficient, GPU-accelerated and biophysically-detailed SNN simulator. PyCARL facilitates joint development of machine learning models and code sharing between CARLsim and PyNN users, promoting an integrated and larger neuromorphic community. Second, we integrate cycle-accurate models of state-of-the-art neuromorphic hardware such as TrueNorth, Loihi, and DynapSE in PyCARL, to accurately model hardware latencies that delay spikes between communicating neurons and degrade performance. PyCARL allows users to analyze and optimize the performance difference between software-only simulation and hardware-software co-simulation of their machine learning models. We show that system designers can also use PyCARL to perform design-space exploration early in the product development stage, facilitating faster time-to-deployment of neuromorphic products. We evaluate the memory usage and simulation time of PyCARL using functionality tests, synthetic SNNs, and realistic applications. Our results demonstrate that for large SNNs, PyCARL does not lead to any significant overhead compared to CARLsim. We also use PyCARL to analyze these SNNs for a state-of-the-art neuromorphic hardware and demonstrate a significant performance deviation from software-only simulations. PyCARL allows to evaluate and minimize such differences early during model development.Comment: 10 pages, 25 figures. Accepted for publication at International Joint Conference on Neural Networks (IJCNN) 202

Run-time Mapping of Spiking Neural Networks to Neuromorphic Hardware

status: publishe

Comparative studies of conventional, organic and natural farming types for their efficiency, and productivity in maize + red gram intercropping system

The field experiment on comparative studies of different farming methods for their efficiency and productivity in maize + redgram intercropping system was conducted at zonal agricultural and horticultural research station, Bhavikere during Kharif season. Among the different farming types, growth and yield parameters of maize viz. plant height (204.18 cm) number of leaves/ plant (17.20), cob length (15.94cm), straw yield (19.35 t/ ha) grain yield (81.36 q/ ha) and red gram Grain yield (4.36 q/ ha) and straw yield (11.19 q/ ha) were significantly higher with treatment received nutrients as per package of practices as compared to natural farming and organic farming treatments. Similar trend was observed with maize equivalent yield (95.50 q/ ha). On the other hand, highest dehydrogenase (14.32, 28.65, 24.19 and 16.23 µg TPF/ g soil/ day) and urease (4.12, 12.65, 7.14 and 3.32 µg NH4-N/ g / soil/ 2 hrs) enzyme activity was observed in organic farming treatment at 30, 60, 90 DAS and at harvest, respectively followed by natural farming treatment and least enzyme activity was noticed in farmers practice. Same trend was observed in acid and alkaline phosphatase enzyme activities. There was no much variation in physical properties i.e., bulk density, particle density, maximum water holding capacity and porosity by the different treatments and also no significant difference occurs in the pH and EC, however higher nitrogen (315.27) phosphorus (73.48) and potassium (271.28) was observed in the organic farming treatment and it was followed with the farmer’s practice treatment. The lowest was recorded in the natural farming treatment (215.47, 33.47 and 220.47 at the harvest stage)