IEEE Solid-State Circuits Magazine - Spring 2016 - 59

In-Situ
Computations,
e.g., Dot Products for
Machine Learning

Processor

DIMMs with
Memory and Logic

3D-Stacked Devices
with Memory and Logic

Logic for Features

Processing Features

Auxiliary

Fixed-Function Units
General-Purpose Cores
Accelerators
Reconfigurable Logic

Compression
Timing
Reliability
Security

Figure 1: An overview of feature-rich memory architectures.

detailed in the introduction, the need
for value additions in the memory
system. The area has also broadened
its scope-processors may be placed
in a 3D-stacked package [28], [21], on
a DIMM [27], and on solid-state drive
(SSD) devices [7]. Accordingly, the term
near data processing (NDP) is more
descriptive and accurate than PIM.
NDP research has been in the spotlight
recently, with multiple papers at toptier venues, a very successful series
of workshops (WoNDP) at the International Symposium on Microarchitecture, and a special issue in IEEE Micro.
Indeed, research in this area has closely
tracked the famous Gartner hype curve
[11], with a steep rise, a steep fall,
and, hopefully now, a period of robust
enlightenment and productivity.
So why is NDP useful? Consider
an application that is searching for a
particular record in a data set. Conventional architectures would move
the entire data set to the processor,
consuming all the memory bandwidth,
expending large amounts of data
movement energy and polluting the
processor caches, only to isolate a few
records of interest. This is the classic
killer app for NDP. Such a "filtering"
operation can be performed by a simple processor on the memory device.

The near-data processor enjoys both
lower latency and lower energy for
memory access and higher memory
bandwidth. Meanwhile, the processor's
caches and memory bandwidth can be
better used for other relevant data sets
that exhibit higher locality.
Clearly, several research issues
need to be addressed to realize the
potential of NDP. Below, each of these
issues are listed, along with example
attempts to address them. While most
of this discussion uses near-DRAM processing as a driving example, note that

M

M

M

P

P

P

similar approaches can also be used for
near-NVM and near-flash processing.

What Workloads Can
Benefit from NDP?
To answer this, we first identify the
types of computations that can be
meaningfully off-loaded to the memory device. As a strawman, consider
the generic NDP architecture in Figure 2, which includes a network
of processor sockets and memory
devices, each with multiple processing
cores. This is essentially a distributed

M
B/4

B/4

M
A

P

C/2

P

M
C/2

P

Processor
Socket
P

P

P

M

M

M

B/4

B/4

Other
Processors

P

P

P

M

M

M

A: Intrapackage Memory Bandwidth
B: Processor Socket External Bandwidth
C: Memory Device External Bandwidth
Bandwidth: A > B > C

Memory
Device

Figure 2: A generic bandwidth model for NDP.

IEEE SOLID-STATE CIRCUITS MAGAZINE

S P R I N G 2 0 16

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