Design of Hardware Accelerated Co‑Processor for Neural Inference

Computations

Kushagra Chauhan and R. Vidhyaand S. Nagadevi

Department of Computing Technologies, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, 603203,

Tamil Nadu, India

Keywords: ASIC, Neural Networks, Hardware Acceleration, Large Language Models, Energy Efficiency.

Abstract: The rapid growth in AI model complexity has increased demand for hardware capable of high- efficiency

inference with lower energy consumption. While GPUs have traditionally driven AI advancements, their

progress has plateaued with compute density improving by only 15% over four years. This work develops a

highly optimized ASIC accelerator specifically for LLaMA 3.1, implementing critical inference operations

through specialized computational units and memory hierarchy. The de sign achieves 100% efficiency in

matrix multiplication operations and 99.90% efficiency in attention computations, demonstrating significant

improvements over traditional GPU implementations. Results show sustained throughput of 256 operations

per cycle in matrix multiplication and 7.99 operations per cycle for attention mechanisms, with memory

bandwidth utilization of 1024GB/s. This research presents a sustainable solution for large-scale AI inference

deployment, addressing both computational efficiency and energy consumption challenges.

1 INTRODUCTION

The significant proliferation of AI technologies has

led to great demands for specialized hardware to

perform efficient and sustainable intensive model

inference. While GPUs form part of traditional

computing solutions and have widely played

significant roles in the advancement of AI capabilities,

they get used for general applications, leading to

suboptimal performance in AI applications. As model

sizes continue to balloon exponentially, energy and

computational requirements in data centers are

growing at an unprecedented pace (Y. LeCun et al.

2015).

By the end of 2024, Microsoft plans to deploy

approximately 1.8 million AI chips, primarily

NVIDIA H100 GPUs, resulting in a total power

consumption of around 1260MW for GPU processing

alone. When accounting for an additional 75% energy

overhead for cooling, networking, and other

supporting services, Microsoft’s AI infrastructure

will require 2205MWh. Similarly, Meta’s AI

infrastructure will consume around 420MW of power

for GPU computation and 735MWh in total with

overheads. The economics of scale of AI is moving

fast. The costs of training state-of-the-art models run

to billions of dollars, generating over $10 billion in

inference costs. At this scale, even an efficiency gain

of 1% would justify investments of $50-$100 million

in custom chip development. Performance of ASICs

has shown benefits of 10-100x over GPUs, as

demonstrated in Bitcoin mining applications (N. P.

Jouppi et al. 2017)

2 RELATED WORKS

The von Neumann bottleneck presents a

fundamental challenge in neural accelerators,

where bandwidth between processors and

memory becomes an overwhelming performance

barrier (D. Markovi´c et al., 2020). This

architectural limitation particularly affects neural

network accelerators where the volume and

frequency of data movement between memory

and processing units create significant

performance barriers.

Chen et al. 2016 introduced Eyeriss,

demonstrating significant energy efficiency

improvements through careful dataflow

optimization. Their spatial architecture achieved

1.4× to 2.5× better efficiency for AlexNet layers

Chauhan, K. and Nagadevi, R. V. S.

Design of Hardware Accelerated Co-Processor for Neural Inference Computations.

DOI: 10.5220/0013921700004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 5, pages 5-10

ISBN: 978-989-758-777-1

and up to 3.7× fewer DRAM accesses compared

to contemporary solutions.

A significant milestone in neural network

acceleration came with Google’s Tensor

Processing Unit, analyzed in detail by Jouppi et

al. 2017. Their implementation validated the

crucial role of systolic arrays in neural network

acceleration, particularly for repeated matrix

multiplication operations common in inference

tasks. The TPU architecture demonstrated that

careful optimization of matrix operations and

weight reuse strategies could achieve up to 3.5

times better performance per watt compared to

contemporary GPU solutions.

The introduction of Transformer architecture

by Vaswani et al. revolutionized natural language

processing while presenting new challenges for

hardware acceleration. The attention mechanisms

require specialized computation patterns distinct

from traditional neural network operations,

introducing additional complexity in hardware

realization (S. Han et al. 2016).

3 SYSTEM ARCHITECTURE

Overall Architecture: The accelerator architecture

centers around a lightweight core that manages

control flow and coordinates specialized

computational units. The system comprises three

primary components integrated through a

sophisticated FIFO-based communication system:

Sophisticated memory hierarchy with 32 independent

banks, High-throughput matrix multiplication unit

based on systolic arrays, Specialized attention module

supporting 64 parallel heads.

Figure 1 show the

High-level architecture of the proposed accelerator

showing major components and their

interconnections.

Figure 1: High-level architecture of the proposed

accelerator showing major components and their

interconnections.

Memory Hierarchy Implementation: The memory

subsystem implements a multi-bank architecture

comprising 32 independent memory banks, optimized

for the high bandwidth requirements of neural

network inference. The design includes:

Bank Organization: The bank organization features

32 independent memory banks that operate in

parallel, enabling efficient and simultaneous data

processing. Each bank is equipped with 64-byte cache

lines specifically optimized for matrix operations,

ensuring high performance in computational tasks

that rely heavily on such calculations. Additionally,

the system incorporates bank-level buffering, which

significantly reduces access latency by allowing

smoother and faster data retrieval. To further enhance

efficiency, advanced conflict resolution mechanisms

are integrated, minimizing delays and ensuring

seamless operation across the parallel banks.

Memory Controller: The memory controller is

designed with a sophisticated scheduling system that

ensures efficient management of data flow across the

memory banks. It employs a round-robin bank access

allocation strategy, which evenly distributes access to

the banks, promoting balanced performance and

preventing bottlenecks. To maintain precise timing

management, the controller includes latency counters

that track and optimize access delays, ensuring

smooth operation. Additionally, write-back policies

are tailored specifically for neural operations,

enhancing the system's ability to handle the demands

of neural network computations effectively.

Matrix Multiplication Unit: The matrix

multiplication unit implements a 16×16 systolic array

architecture designed for optimal throughput and

resource utilization.

Processing Element Design: Each processing

element is equipped with a two-stage pipeline

architecture, enabling efficient execution of

instructions by overlapping stages of processing for

improved throughput. It includes local accumulator

arrays, which provide fast, on-chip storage for

intermediate results, reducing the need for frequent

memory access. The processing elements also feature

input queues for data buffering, allowing smooth and

continuous data flow to prevent stalls during

operation. Additionally, the SIMD-compatible data

paths ensure that the system can perform parallel data

processing, making it well-suited for tasks that

benefit from single instruction, multiple data

operations.

Control Flow Management: The control system

implements a distributed control architecture,

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allowing for decentralized management that enhances

scalability and reduces single-point failures across the

system. It incorporates lightweight synchronization

protocols, which streamline coordination between

components

while

minimizing

overhead,

ensuring

efficient

operation.

Advanced

pipeline

management is also a key feature, optimizing the flow

of instructions and data through the processing stages

for maximum throughput. Additionally, the system

includes performance monitoring capabilities,

enabling real-time tracking and analysis of

operational efficiency to support ongoing

optimization and troubleshooting.

Attention Module Implementation: The attention

module implements 64 parallel attention heads, each

capable of independent computation of attention

patterns.

Attention Head Architecture: Each attention head

contains dedicated Q/K/V computation units, which

are specialized for calculating the query, key, and

value components essential to attention mechanisms,

ensuring precise and efficient processing. These

heads also feature double-buffered computation units,

allowing seamless switching between buffers to

minimize downtime and enhance computational

continuity. An optimized softmax implementation is

included, tailored to accelerate the normalization

process critical for attention-based operations.

Furthermore, stream-aligned memory access patterns

are utilized, aligning data retrieval with processing

needs to maximize efficiency and reduce latency

during memory interactions.

Parallel Processing Management: The module

implements independent operation of attention heads,

enabling each head to function autonomously, which

enhances parallelism and overall system efficiency. It

incorporates sophisticated synchronization

mechanisms to coordinate activities across these

heads seamlessly, ensuring consistent and reliable

performance. Dynamic workload distribution is also a

key feature, allowing the module to adaptively

allocate tasks based on real-time processing demands,

optimizing resource utilization. Additionally,

advanced flow control is employed to manage data

movement effectively, preventing bottlenecks and

maintaining smooth operation throughout the

module's processes.

4 RESULTS AND

PERFORMANCE ANALYSIS

Test Environment: The performance evaluation was

conducted under rigorous test conditions to

thoroughly assess the system's capabilities. It utilized

a hidden size of 8192, reflecting a substantial capacity

for feature representation within the model. The

evaluation included 64 attention heads, allowing for

extensive parallel processing and attention coverage

across the data. A batch size of 32 was employed,

striking a balance between computational efficiency

and memory usage. Additionally, a sequence length

of 2048 was tested, simulating the system's ability to

handle long-range dependencies and large-scale input

data effectively.

Matrix Multiplication Performance: The matrix

multiplication unit achieved exceptional performance

metrics as shown in Table 1.

Table 1: Matrix multiplication unit performance.

Metric Value Status

Active Cycles 1010/1010 Optimal

Efficiency 100.00% Optimal

Operations 2,58,560 High

Throughput 256.00 ops/cycle Optimal

Bandwidth 1024 GB/s Optimal

Attention Mechanism Performance: The attention

module demonstrated impressive efficiency metrics

as shown in Table 2.

Table 2: Attention unit performance.

Metric Value Status

Active C

cles 1009/1010 Hi

Efficienc

99.90% Hi

erations 8,072 Normal

Throughput 7.99 ops/cycle Goo

System-Level Performance: The complete system

demonstrated several key performance characteristics

that highlight its robustness and efficiency. It

achieved sustained throughput across varying

workloads, ensuring consistent performance

regardless of task complexity or demand fluctuations.

The system exhibited minimal pipeline stalls,

reflecting a well-optimized design that keeps

processing stages active and reduces idle time.

Efficient memory bandwidth utilization was another

standout feature, maximizing data transfer rates while

minimizing waste, which is critical for high-

performance operations. Additionally, effective

thermal management ensured that the system

Design of Hardware Accelerated Co-Processor for Neural Inference Computations

maintained optimal operating temperatures,

enhancing reliability and longevity under intense

computational loads.

4.1 Detailed Evaluation Methodology

Testing Framework: The evaluation framework was

implemented in SystemC, utilizing a comprehensive

test bench designed to provide detailed and reliable

analysis of the system. It enables cycle-accurate

simulation of all components, ensuring precise

modeling of timing and behavior across the entire

architecture. Performance counter integration at

multiple levels allows for granular tracking of key

metrics, offering deep insights into system efficiency

and bottlenecks. The framework also supports

configurable workload generation, providing

flexibility to simulate a wide range of scenarios

tailored to specific testing needs. Additionally,

automated regression testing is incorporated,

streamlining the validation process and ensuring

consistent performance across updates or

modifications.

Workload Generation: Test vectors were generated

to align with LLaMA 3.1’s computational patterns,

ensuring the evaluation closely mirrored real-world

usage of the model. These included matrix sizes

matching the model’s dimensions, specifically

8192×8192, to replicate the scale of its matrix

operations accurately. Attention patterns were

derived from real inference scenarios, capturing the

system’s behavior under practical attention-based

workloads. Variable sequence lengths ranging from

128 to 2048 were tested, assessing the system’s

adaptability to both short and extended input

sequences. Additionally, different batch sizes—1, 8,

16, and 32 were incorporated, allowing the evaluation

to explore performance across a spectrum of

processing demands.

Performance Metrics: During the evaluation,

several critical metrics were collected to assess the

system's performance comprehensively.

Computational throughput, measured in operations

per cycle (ops/cycle), provided insight into the

system’s processing speed and efficiency. Memory

bandwidth utilization was tracked to evaluate how

effectively the system leveraged available memory

resources, a key factor in data-intensive tasks.

Pipeline stall statistics were gathered to analyze

interruptions in the processing flow, highlighting the

system’s ability to maintain continuous operation.

Additionally, power efficiency estimates were

calculated, offering a view into the system’s energy

consumption relative to its performance, an essential

consideration for scalability and sustainability.

4.2 SystemC Implementation Details

The accelerator was implemented using SystemC

2.3.3, enabling cycle-accurate modeling of

hardware behavior while maintaining high-level

C++ abstraction.

Processing Element Design: The Processing

Element serves as the fundamental computational

unit within the systolic array, featuring a meticulously

designed architecture to optimize performance. It

implements a two-stage pipeline that enhances

throughput by allowing overlapping execution of

operations, paired with local storage in the form of an

accumulator and an input queue for efficient data

management and flow control. The operation of the

Processing Element is governed by a structured

algorithm: it initializes with class members including

clock, reset, inputs a and b, and an output, alongside

internal states like the accumulator, a busy flag, and

the input queue. Upon initialization, the busy flag is

set to false, and the compute and input-handling

processes are registered to trigger on the clock’s

positive edge and input changes, respectively. The

compute procedure, central to its functionality, resets

the accumulator to zero and clears the busy state if a

reset signal is detected; otherwise, when the input

queue is not empty and the unit is not busy, it retrieves

data from the queue’s front, performs a multiply-

accumulate operation (accumulator ← accumulator +

data.a × data.b), removes the processed data from the

queue, and writes the updated accumulator value to

the output. This design ensures efficient, continuous

processing with robust control over data flow and

computational state.

Memory Bank Operation: The Memory Bank

controller is engineered to manage access patterns

and prefetch operations, ensuring optimal bandwidth

utilization across the system. It orchestrates the

interaction between the memory banks and the

processing units by intelligently scheduling data

retrieval and storage, minimizing latency and

maximizing throughput. Through sophisticated

prefetching, the controller anticipates data needs

based on access patterns, proactively loading relevant

data into buffers to reduce wait times and enhance

overall efficiency. This design enables the Memory

Bank to support high-performance operations,

particularly in data-intensive tasks, by maintaining a

steady and effective flow of information to and from

the memory banks.

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Attention Head Implementation: The Attention

Head is designed to implement parallel processing of

attention computations, incorporating optimizations

for numerical stability to ensure both efficiency and

accuracy. It handles the core attention mechanism by

simultaneously computing query, key, and value

operations across dedicated units, leveraging

parallelism to accelerate processing speed. Numerical

stability optimizations are integrated into the design,

such as an enhanced softmax implementation, which

mitigates issues like overflow or underflow during

normalization, preserving the precision of attention

scores even under large-scale or extreme data

conditions. This combination of parallel execution

and stability-focused enhancements enables the

Attention Head to deliver robust and reliable

performance in attention-based workloads.

Performance Monitoring Implementation: The

Performance Monitoring System is designed to

provide a detailed assessment of system performance

through a structured set of metrics and procedures. It

tracks primary metrics such as cycles, active cycles,

stall cycles, and operations, while also calculating

derived metrics including throughput, efficiency, and

bandwidth. The Update Metrics procedure computes

these derived values: throughput is determined as

operations ÷ cycles, efficiency is calculated as (1 −

stall cycles ÷ cycles) × 100 to express the percentage

of productive time, and bandwidth is derived as bytes

transferred ÷ (cycles × CYCLE_TIME_NS) × 10⁻⁹,

resulting in a gigabytes-per-second (GB/s)

measurement. The GenerateReport procedure

enhances this analysis by calling Update Metrics and

logging a comprehensive performance report for the

specified module. This report includes key statistics:

total cycles, active cycles, stall cycles, operations,

throughput (in ops/cycle), efficiency (as a

percentage), and bandwidth (in GB/s). By

systematically logging these metrics, the system

ensures that performance data is both accurately

captured and easily accessible for optimization and

debugging purposes.

4.3 Implementation Path and Future

Development

Hardware Implementation Considerations: The

transition from SystemC simulation to hardware

implementation presents several challenges,

primarily in fabrication technology selection and

design adaptation. Fabrication choices range from

advanced nodes such as 3nm and 5nm from major

foundries to more mature nodes like 28nm and 45nm

for initial prototyping, with FPGA implementation

serving as a viable option for validation. Additionally,

adapting the design requires careful consideration of

scaling for available process nodes, power and

thermal management, physical design constraints,

and rigorous testing and validation requirements.

To further enhance hardware performance,

several improvements can be incorporated. These

include the integration of advanced memory

technologies, optimized implementations of attention

mechanisms, sophisticated power management

systems, and effective thermal optimization

techniques. These enhancements contribute to better

efficiency, performance, and scalability of the final

hardware implementation.

Software Stack Development: The software

infrastructure for the system requires a well-

structured development approach, starting with the

Driver Layer, which must ensure efficient data

transfer mechanisms, robust command execution

management, system monitoring capabilities, and

effective error handling with recovery mechanisms.

Additionally, Framework Integration is essential,

involving seamless interfaces with PyTorch and

TensorFlow, custom operator implementations,

workload-specific optimizations, and performance

monitoring tools. Looking ahead, future development

will prioritize comprehensive driver enhancements,

further optimizations for framework integration,

advanced performance monitoring, and improved

debugging and profiling capabilities to ensure a

highly efficient and scalable software stack.

5 CONCLUSIONS

This study illustrates the effective design and

modelling of a high-performance ASIC accelerator

for neural network inference. The implementation

demonstrates outstanding performance metrics, with

the matrix multiplication unit operating at full

efficiency at 256 operations per cycle and achieving

99.90% operational efficiency in the attention

module.

The research has accomplished several notable

accomplishments. The project encompasses the

creation of a comprehensive SystemC model for an AI

accelerator, allowing precise simulation and

validation. A highly efficient 32-bank memory

system has been developed to enhance data access

and processing performance. The research includes

the construction of a systolic array-based matrix

multiplication unit, improving computing efficiency

for AI tasks. Additionally, a parallel attention

Design of Hardware Accelerated Co-Processor for Neural Inference Computations

mechanism with 64 heads has been implemented,

enhancing the model's capacity to manage intricate

attention-based calculations.

This study significantly advances the domain of AI

hardware acceleration by investigating feasible

alternatives to GPU-based inference, providing

energy-efficient solutions designed for data centres,

and laying a solid framework for future ASIC

development. Furthermore, it tackles the von

Neumann bottleneck in neural computing, facilitating

the development of more efficient and scalable AI

processing systems.

This study presents several intriguing directions

for further investigation. A primary focus is the

incorporation of developing AI model designs, which

may significantly improve performance and

efficiency. Furthermore, investigating sophisticated

power optimization methods might result in more

energy-efficient applications. Enhanced memory

management techniques may enhance resource

consumption, allowing improved scalability and

accelerated calculations. Ultimately, optimizing

attention mechanism implementations may improve

model precision and processing efficiency, making

AI systems more effective and versatile in

handling intricate jobs. The tangible implementation

of AI accelerators signifies a vital advancement

toward enhanced and more accessible AI computing.

This study lays the groundwork for future

advancements, considering manufacturing limitations

and the methodical creation of software infrastructure

for effective neural network acceleration solutions.

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