In-Memory Special Function Unit Offloads Complex Math Operations
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Solution Overview
Problem
In computing environments, processors face limitations in performance due to limited communication bandwidth with memory and high energy consumption, particularly when processing complex operations like square root, reciprocal, log, exponential, and trigonometric functions, which are inefficiently handled by software libraries.
Innovation Solution
A computing system with a host processor offloads specific operations to an internal processor-in-memory (PIM) with a special function unit (SFU), bypassing the cache and directly processing these operations in memory, reducing memory access bandwidth and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If complex operations (square root, reciprocal, log, exponential, trigonometric functions) are processed using software libraries on the host processor, then processing flexibility is maintained, but processing speed and energy efficiency deteriorate
Solution Approach 1:
The host processor is segmented from the memory system by introducing a dedicated internal processor within the memory device. Complex operations are separated from general-purpose processing and assigned to specialized hardware logic (SFU) in the internal processor, enabling parallel execution and improving processing speed while reducing host processor burden.
Solution Approach 2:
An internal processor is introduced as an intermediary between the host processor and memory array. This intermediary contains a special function unit that handles complex mathematical operations, acting as a bridge that offloads computation-intensive tasks from the host processor while maintaining close proximity to data storage.
2Productivity
If the host processor processes all operations, then processing control is centralized, but communication bandwidth with memory becomes a bottleneck
Solution Approach 1:
The internal processor merges computation and memory access functions by placing a special function unit directly within the memory device. This allows complex operations to be performed on data while it resides in memory, eliminating the need for repeated data transfers between host processor and memory, thereby reducing communication bandwidth consumption and energy usage.
Solution Approach 2:
The architecture transitions from a single-processor model to a distributed processing model by adding a second processing dimension within the memory device. The internal processor operates in parallel with the host processor, creating a two-level processing hierarchy that increases overall throughput without saturating the host-memory communication channel.
3Use of energy by moving object
If the host processor handles all computational tasks, then processing uniformity is maintained, but energy consumption increases
Solution Approach 1:
Different parts of the processing system are assigned different qualities and functions. The host processor maintains general-purpose control and coordination capabilities, while the internal processor's special function unit provides localized high-performance computation for specific mathematical operations. This division allows energy-efficient processing by matching task requirements to appropriate processing resources.
Solution Approach 2:
The system dynamically routes computational tasks based on their nature. Simple operations continue to be handled by the host processor maintaining processing uniformity, while complex mathematical operations are dynamically offloaded to the internal processor's SFU. This dynamic task allocation reduces overall energy consumption while preserving the system's versatility through adaptive task distribution.
Data Source
AI summary
A computing system includes a host processor configured to process operations and a memory configured to include an internal processor and store host instructions to be processed by the host processor. The host processor offloads processing of a predetermined operation to the internal processor. The internal processor possibly provides specialized hardware designed to process the operation efficiently, improving the efficiency and performance of the computing system.