Phase Change Memory Shows Promise for AI Use Says IBM

By John Russell

October 24, 2018

Research into phase change memory (PCM) technology and its application to neuromorphic computing isn’t new, but it has steadily progressed with IBM playing an important role. Earlier this month, IBM researchers published a progress report in the AIP Journal of Applied Physics describing how PCM devices could implement select AI functions effectively and at lower power than traditional CMOS von Neumann circuits.

“We are on the cusp of a revolution in AI,” argue the authors. Near-term AI will depend on traditional CMOS circuits but then shift to include non-von Neumann ‘co-processing’ approaches.

“By augmenting conventional computing systems,” they wrote, “these systems could help achieve orders of magnitude improvement in performance and efficiency. In summary, we believe that we will see two stages of innovations that take us from the near term, where the AI accelerators are built with conventional CMOS, towards a period of innovation involving the computational approaches presented in this article.”

Time will tell if they are correct. The IBM researchers take pains to show how PCM devices can be used for in-memory computing functions such as matrix-vector multiplication, unsupervised learning, deep learning, spiking neural networks, and more. They also identify and briefly discuss the challenges, however the concrete examples presented suggest PCM technology is nearing viability for use in AI and other in-memory computing functions.

The IBM paper – Brain-inspired computing using phase-change memory devices by Abu Sebastian, Manuel Le Gallo, Geoffrey W. Burr, Sangbum Kim, Matthew BrightSky, and Evangelos Eleftheriou – is an interesting accessible read.

This illustration taken from the paper (below) shows their view of how technology use is divided among AI categories.

There’s also an article on the work (A New Brain-Inspired Architecture Could Improve How Computers Handle Data and Advance AI) posted on the AIP Publishing site in which Sebastian emphasized that executing certain computational tasks in the computer’s memory would increase the system’s efficiency and save energy:

“If you look at human beings, we compute with 20 to 30 watts of power, whereas AI today is based on supercomputers which run on kilowatts or megawatts of power,” Sebastian said in the article. “In the brain, synapses are both computing and storing information. In a new architecture, going beyond von Neumann, memory has to play a more active role in computing.”

The IBM team drew on three different levels of inspiration from the brain. The first level exploits a memory device’s state dynamics to perform computational tasks in the memory itself, similar to how the brain’s memory and processing are co-located. The second level draws on the brain’s synaptic network structures as inspiration for arrays of phase change memory (PCM) devices to accelerate training for deep neural networks. Lastly, the dynamic and stochastic nature of neurons and synapses inspired the team to create a powerful computational substrate for spiking neural networks.

By way of background, phase change memory is “based on the property of certain compounds of Ge, Te, and Sb that exhibit drastically different electrical characteristics depending on their atomic arrangement. In the disordered amorphous phase, these materials have very high resistivity, while in the ordered crystalline phase, they have very low resistivity.”

As described the authors, “When a current pulse of sufficiently high amplitude is applied to the PCM device (typically referred to as the RESET pulse), a significant portion of the phase change material melts owing to Joule heating. The typical melting temperature of phase-change materials is approx. 600 C. When the pulse is stopped abruptly so that temperature inside the heated device drops rapidly, the molten material quenches into the amorphous phase due to glass transition. In the resulting RESET state, the device will be in a high resistance state if the amorphous region blocks the bottom electrode.”

PCM has two critical properties governing its use in circuits:

  • The first key property of PCM that enables brain-inspired computing is its ability to achieve not just two levels but a continuum of resistance or conductance values. This is typically achieved by creating intermediate phase configurations by the application of suitable partial RESET pulses
  • The second key property that enables brain-inspired computing is the accumulative behavior arising from the crystallization dynamics… [O]ne can induce progressive reduction in the size of the amorphous region (and hence the device resistance) by the successive application of SET pulses with the same amplitude. However, it is not possible to achieve a progressive increase in the size of the amorphous region. Hence, the curve shown in Fig. 3(c) typically referred to as the accumulation curve, is unidirectional.

Using these properties it is possible to implement a variety of logical, arithmetic, and machine learning functions with PCM memory.

Compressed sensing and recovery, say the researchers, is one of the applications that could benefit from a computational memory unit that performs matrix-vector multiplications. They note:

“The objective behind compressed sensing is to acquire a large signal at a sub-Nyquist sampling rate and subsequently reconstruct that signal accurately. Unlike most other compression schemes, sampling and compression are done simultaneously, with the signal getting compressed as it is sampled. Such techniques have widespread applications in the domains of medical imaging, security systems, and camera sensors.

“The compressed measurements can be thought of as a mapping of a signal x of length N to a measurement vector y of length M < N. If this process is linear, then it can be modeled by an M N measurement matrix M. The idea is to store this measurement matrix in the computational memory unit, with PCM devices organized in a cross-bar configuration [see Fig. 6(a)]. This allows us to perform the compression in O(1) time complexity. An approximate message passing algorithm (AMP) can be used to recover the original signal from the compressed measurements, using an iterative algorithm that involves several matrix-vector multiplications on the very same measurement matrix and its transpose. In this way, we can also use the same matrix that was coded in the computational memory unit for the reconstruction, reducing the reconstruction complexity.”

Shown here is an experimental illustration of compressed sensing recovery in the context of image compression. A 128 x128 pixel image was compressed by 50% and recovered using the measurement matrix elements encoded in a PCM array.

A key challenge with computational memory, concede the researchers, is the lack of high precision. Even though approximate solutions are sufficient for many computational tasks in the domain of AI, there are some applications that require that the solutions are obtained with arbitrarily high accuracy.

“Fortunately, many such computational tasks can be formulated as a sequence of two distinct parts. In the first part, an approximate solution is obtained; in the second part, the resulting error in the overall objective is calculated accurately. Then, based on this, the approximate solution is refined by repeating the first part. Step I typically has a high computational load, whereas Step II has a light computational load. This forms the foundation for the concept of mixed-precision in-memory computing: the use of a computational memory unit in conjunction with a high-precision von Neumann machine.23 The low-precision computational memory unit can be used to obtain an approximate solution as discussed earlier. The high-precision von Neumann machine can be used to calculate the error precisely. The bulk of the computation is still realized in computational memory, and hence we still achieve significant areal/power/ speed improvements while addressing the key challenge of imprecision associated with computational memory),” they wrote.

You get the flavor of the paper. It is an interesting overview, best read directly, showing the promising progress made in formulating ideas for structuring PCM and PCM-cum-traditional circuits for implementing AI functions.

Link to paper:

Link to AIP article on the work:

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