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Dharmendra S. Modha

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Exciting Opportunities in Brain-inspired Computing at IBM Research

September 5, 2019 By dmodha

Opportunities:

Here are the current opportunities at IBM Research – Almaden in San Jose, California to extend the frontier of computing:

  • Research Scientist (Master’s/PhD degree)
  • Logic Design/RTL Hardware Engineer (Bachelor’s/Master’s degree, Master’s/PhD degree)
  • Physical Chip Design Hardware Engineer (Bachelor’s/Master’s degree, Master’s/PhD degree)
  • Verification/Logic Design Hardware Engineer (Bachelor’s/Master’s degree, Master’s/PhD degree)
  • Software Engineer – C++ simulation (Bachelor’s degree, Master’s degree)
  • Research Staff Member (Master’s/PhD degree)


Background:

Working since 2004, IBM’s Brain-Inspired Computing team at IBM Research – Almaden is a pioneer in neuromorphic computing.

The project has received ~$85 million in research funding from DARPA (under SyNAPSE Program), US Department of Defense, US Department of Energy, and commercial customers.

Our team has built TrueNorth (a first-of-a-kind modular, scalable, non-von Neumann, ultra-low power, cognitive computing architecture), associated end-to-end software ecosystem, and many systems. TrueNorth and its ecosystem are in the hands of ~200 researchers at ~45 institutions on five continents.

The team has been recognized with the Misha Mahowald Prize, ACM Gordon Bell Prize, R&D 100 Award, Best Paper Awards at ASYNC and IDEMI conferences, and First Place, Science/NSF International Science & Engineering Visualization Contest. TrueNorth has been inducted into the Computer History Museum.

Our papers have been published in Science, Communications of the ACM, PNAS, amongst many other venues. Our work has been featured in The Economist, Science, New York Times, Wall Street Journal, The Washington Post, BBC, CNN, PBS, PBS NOVA, Discover, MIT Technology Review, Associated Press, Communications of the ACM, IEEE Spectrum, Forbes, Fortune, Time, amongst many other media outlets.

“The best way to predict your future is to create it.” ― Abraham Lincoln

Join us!

Filed Under: Brain-inspired Computing

The TrueNorth Journey: 2008 – 2018 (video)

July 2, 2019 By dmodha

Filed Under: Brain-inspired Computing, Videos

IEEE Computer Cover Feature — TrueNorth: Accelerating From Zero to 64 Million Neurons in 10 Years

May 14, 2019 By dmodha

May 2019 issue of IEEE Computer Magazine’s Cover Features highlights an article summarizing ten years of innovation from IBM Research.

Abstract: IBM’s brain-inspired processor is a massively parallel neural network inference engine containing 1 million spiking neurons and 256 million low-precision synapses. Now, after a decade of fundamental research spanning neuroscience, architecture, chips, systems, software, and algorithms, IBM has delivered the largest neurosynaptic computer ever built.

FIGURE 1. The timeline of the development of TrueNorth.
FIGURE 3. The 64-processor scale-out NS16e-4.
FIGURE 5. A decade of neuromorphic applications on TrueNorth.

Filed Under: Accomplishments, Brain-inspired Computing, Papers

Design Awards for NS16e-4 System

March 11, 2019 By dmodha

Guest Post by William P. Risk

Over the course of the SyNAPSE / TrueNorth project, we’ve had the opportunity to leverage the technical depth and breadth that exist in IBM, both within and outside the Research Division. In particular, we’ve collaborated with IBM’s industrial design team since the early days of the project, when they helped us imagine and communicate potential applications of this new technology through concept models and created an iconic cap for the TrueNorth chip. Most recently, we’ve collaborated with both our industrial designers and our Systems Group engineers to design the landmark NS16e-4 Neurosynaptic System.

The elegant and iconic design of this system was recognized recently with two design awards.

First, it was named a Featured Finalist in the 2018 International Design Excellence Awards of the Industrial Design Society of America.

Second, it received 2019 iF Design Award from the iF Design Foundation.

Filed Under: Accomplishments, Brain-inspired Computing, Collaborations, Prizes

PREPRINT: Low Precision Policy Distillation with Application to Low-Power, Real-time Sensation-Cognition-Action Loop with Neuromorphic Computing

October 8, 2018 By dmodha

Guest Blog by Deepika Bablani

Title: Low Precision Policy Distillation with Application to Low-Power, Real-time Sensation-Cognition-Action Loop with Neuromorphic Computing

Authors: Jeffrey L. McKinstry, Davis R. Barch, Deepika Bablani, Michael V. Debole, Steven K. Esser, Jeffrey A. Kusnitz, John V. Arthur, Dharmendra S. Modha

Abstract: Low precision networks in the reinforcement learning (RL) setting are relatively unexplored because of the limitations of binary activations for function approximation. Here, in the discrete action ATARI domain, we demonstrate, for the first time, that low precision policy distillation from a high precision network provides a principled, practical way to train an RL agent. As an application, on 10 different ATARI games, we demonstrate real-time end-to-end game playing on low- power neuromorphic hardware by converting a sequence of game frames into discrete actions.

Link: https://arxiv.org/pdf/1809.09260.pdf 

Interest in developing algorithms for energy efficient training and deployment of deep neural networks is on the rise. However, the domain of sequential decision making using RL has been relatively unexplored in this context. This is an interesting avenue for energy efficient deployment as it provides a means for deploying end to end learning agents in real world problems acting in real-time. We demonstrate, for the first time, an RL agent trained using policy distillation[1] to play ATARI games mapped to TrueNorth neuromorphic hardware. Our approach is agnostic to choice of RL algorithms and can be applied to any value based RL algorithm.

Figure 1:  Real-time Sensation-Cognition-Action loop with TrueNorth allows real world deployment of RL models.The TrueNorth system receives as input the game frame (sensation), generates predicted return for all actions (cognition) and outputs the best move based on its learned policy (action) in real time. 

Why RL is challenging for low precision

RL solves a sequential decision making task where an agent interacts with its environment over discrete time steps, and chooses actions to maximize expected long term return [2]. Here we consider the ATARI domain. At every time step, the agent receives from the game simulator a game screen image as input. Using this, along with  preceding frames, the optimal action is chosen from a discrete set to receive a reward.

Deep regression networks have been used to successfully approximate Q (state-action value) functions in value based RL[3]. These networks take raw game frames as input and predict the Q value of each action in the input state, which is used to choose the optimal policy by balancing exploration and exploitation. This is challenging for low precision networks. Q values are continuous, making value based RL a challenging regression task. To obtain the same accuracy as a network with  neurons and rectified linear unit (ReLU) activations, a network with binary activations requires on the order of  neurons[7], Furthermore, solving an RL problem in this constrained space is inherently hard owing to non-stationary data distribution, limited feedback and delayed rewards [3], and the amount of time required by back-propagation can be prohibitively high. 

Our Approach – Low Precision Policy Distillation

Policy distillation[1] focuses on transferring knowledge from a learned policy (teacher) to a new network (student) as a supervised learning problem. The student is trained to mimic the teacher by learning to match the teacher’s actions. As the student has access to teacher’s labels, this helps overcome some challenges associated with RL. By adjusting the temperature in the distillation loss, the labels are made sharper, bringing it closer to supervised classification which is easier for a constrained student. 

We used Double Deep Q Networks (DDQN)[8] to train the full precision teacher and policy distillation on data generated by the teacher to train the student. If the teacher accurately approximates the Q function, then an accurate policy can be derived from it, providing the labels to train the student network. By using policy, the final network is likely to be smaller and faster to train. 

Figure 2:Low precision policy distillation allows principled training of constrained networks for neuromorphic systems.(Top) Full precision teacher network trained using value based RL. (Bottom) Low precision student network is trained on the teacher’s policy and mapped to the TrueNorth platform.

Results on the ATARI benchmark

We demonstrate results on single and multi-task policy distillation. For the single task setting, we use separately trained full precision teacher for each game. The student architecture is similar but wider and deeper. More details about TrueNorth training can be found in [9]. We use two different loss functions – Negative Log Likelihood Loss and KL Divergence Loss and compare single task results. KL Divergence loss gets better scores. 

Figure 3: Low precision student networks trained using KL Divergence loss meet teacher performance. Policy distillation results for online student training (mean score for 100 iterations, normalized against teacher scores) 
Figure 4:Larger student network with sufficient capacity learns to play multiple games.Students trained against 2, 3 or 10 ATARI games. Tested against only those games for which they were trained, performance is normalized against an identical student trained on one game. 

After training, student networks are mapped to TrueNorth using “corelets” [9][10]. The Neurosynaptic System 1 million neuron evaluation platform (NS1e) is a development platform which contains a TrueNorth chip alongside a Xilinx Zynq Z-7020 FPGA. Through tiling, the TrueNorth chips can be directly connected to one another via its native chip-to-chip asynchronous communication interfaces. With this, we have created a platform which natively tiles 16 TrueNorth chips, the NS16e (Neurosynaptic System 16 million neuron evaluation platform), capable of executing networks 16 times larger than those on NS1e.

The Arcade Learning Environment (ALE) was augmented for use by providing hooks into the TrueNorth run-time. We ported the entire ALE game-engine (Stella) and corresponding software modifications so they run on the ARMs while using TrueNorth for inference. The system is able to maintain a frame-rate of 30 frames-per-second(fps). 

This is the first work of its kind to provide an end-to-end solution closing the sensation-cognition-action loop for real time deployment of reinforcement learning algorithms on neuromorphic hardware. This provides a strong baseline to compare future work targeted at closing the gap between algorithmic advances and real world deployment using highly optimized hardware, which is an important challenge in the current research landscape.

All figures in the blog are from the paper.

References

[1] Rusu, A. A.; Colmenarejo, S. G.; Gulcehre, C.; Desjardins, G.; Kirkpatrick, J.; Pascanu, R.; Mnih, V.; Kavukcuoglu, K.; and Had- sell, R. 2015. Policy distillation. arXiv preprint arXiv:1511.06295. 

[2] Sutton, R. S.; Barto, A. G.; et al. 1998. Reinforcement learning: An introduction. MIT press. 

[3] Mnih, V.; Kavukcuoglu, K.; Silver, D.; Graves, A.; Antonoglou, I.; Wierstra, D.; and Riedmiller, M. 2013. Playing Atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602.

[4]Courbariaux, M.; Bengio, Y.; and David, J.-P. 2015. Binarycon- nect: Training deep neural networks with binary weights during propagations. In Advances in neural information processing systems, 3123–3131.

[5] Rastegari, M.; Ordonez, V.; Redmon, J.; and Farhadi, A. 2016. Xnor-net: Imagenet classification using binary convolutional neural networks. In European Conference on Computer Vision, 525– 542. Springer. 

[6] Esser, S. K.; Appuswamy, R.; Merolla, P.; Arthur, J. V.; and Modha, D. S. 2015. Backpropagation for energy-efficient neuromorphic computing. In Advances in Neural Information Processing Systems, 1117–1125

[7] Blum, E. K., and Li, L. K. 1991. Approximation theory and feed- forward networks. Neural networks 4(4):511–515.

[8] Van Hasselt, H.; Guez, A.; and Silver, D. 2016. Deep reinforcement learning with double q-learning. In AAAI, volume 2, 5. Phoenix, AZ. 

[9] Esser, S. K.; Merolla, P. A.; Arthur, J. V.; Cassidy, A. S.; Ap- puswamy, R.; Andreopoulos, A.; Berg, D. J.; McKinstry, J. L.; Melano, T.; Barch, D. R.; di Nolfo, C.; Datta, P.; Amir, A.; Taba, B.; Flickner, M. D.; and Modha, D. S. 2016. Convolutional networks for fast, energy-efficient neuromorphic computing. Proceedings of the National Academy of Sciences 113(41):11441–11446.

[10] Amir, A.; Datta, P.; Risk, W. P.; Cassidy, A. S.; Kusnitz, J. A.; Esser, S. K.; Andreopoulos, A.; Wong, T. M.; Flickner, M.; Alvarez-Icaza, R.; et al. 2013. Cognitive computing programming paradigm: a corelet language for composing networks of neurosy naptic cores. In Neural Networks (IJCNN), The 2013 International Joint Conference on, 1–10. IEEE. 

Filed Under: Brain-inspired Computing, Papers

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